Data management platform

Techniques are disclosed relating to the management of data. A data provider computer system may store particular data of a user. The data provider computer system may commence sharing of a portion of the particular data with a data consumer computer system. The data provider computer system may continue sharing additional portions of the particular data with the data consumer computer system in response to receiving a report from a verification environment indicating that the particular data is being utilized by the data consumer computer system in accordance with a specified usage policy.

BACKGROUND

Technical Field

This disclosure relates generally to a data management platform.

Description of the Related Art

Many companies collect and store data about their users. Such data may include, without limitation, any manner of information, such as user profile information, financial information, medical information, and user activity information (e.g., location data). The creation of such data is growing at an unprecedented rate. Companies often use data to help improve their own systems (e.g., by analyzing the data), or they provide that data to other companies that use the data for some particular purpose. For example, a telecommunication company may analyze geolocation data in order to improve quality of service. Accordingly, data is often considered a valuable resource and thus it is desirable to collect and store. Furthermore, data collected by one entity (e.g., geolocation data collected by a telecommunications company) may often be valuable to another entity (e.g., a retail company that wishes to use the geolocation data to market goods to the user). For this reason, there has been motivation for one company, a “data provider,” to share data of a user or “data subject” with another company, a “data consumer”—an exchange which may be termed a “data economy.”

The collection and management of such data is often problematic for companies, however. Companies are often not aware of all the different types of user data that they have on their systems and even where that data is stored. Thus, it may be extremely difficult for many companies to identify and locate all data of individual customers/users stored across myriad computers and networks within an entity. Accordingly, in many cases, companies cannot benefit from their data if they are not completely aware of it. Still further, even assuming such data can be properly located, ensuring proper internal and external usage of the data—that is, usage that corresponds to company policies for that data—can also be very difficult. As a result, data is often a hindrance to companies, particularly when it is necessary or desirable to share data with another company. The result is that data breaches or misuses are growing increasingly common, and reflect poorly upon the companies that act as the data custodian.

These problems have further been exacerbated with the introduction of various data privacy provisions, including the General Data Protection Regulation (GDPR) promulgated by the European Union. GDPR introduces strict requirements regarding explicit user consent in relation to data usage and ensuring that users or data subjects can request copies of their personal data. Moreover, under the GDPR and other regimes, the question of who owns data has become ambiguous; as such, sharing a user's data without authorization from the user may lead to legal troubles (e.g., large fines) for a company. Accordingly, in this increased regulatory environment, data management has become a cost, a liability, and a headache for many companies that store data about their users, particularly for those companies that lack the mechanisms to protect that data and ensure compliance with various data management regulations and internal policies.

But even apart from the reality that many companies cannot adequately locate and control data about their customers or comply with burgeoning privacy regulations, there is also the fundamental problem that companies are unfairly using the data of their customers to profit by sharing this data with other entities without any form of compensation being provided to the individuals whose data is being used. This allows companies, particularly those that have large amounts of private user data, to reap huge profits by trading on data. Meanwhile, the users are not compensated for such usage, and end up bearing the costs of such usage if a breach or misuse of their data occurs. Each of these issues presents a flaw in today's data economy.

The term “configured to” is not intended to mean “configurable to.” An unprogrammed FPGA, for example, would not be considered to be “configured to” perform some specific function, although it may be “configurable to” perform that function and may be “configured to” perform the function after programming.

As used herein, the terms “first,” “second,” etc. are used as labels for nouns that they precede, and do not imply any type of ordering (e.g., spatial, temporal, logical, etc.) unless specifically stated. As an example, for data that has multiple portions, the terms “first” portion and “second” portion can be used to refer to any portion of that data. In other words, the first and second portions are not limited to the initial two portions of the data.

DETAILED DESCRIPTION

The present disclosure describes various techniques for enabling a data provider system to obtain authorization from a user to share particular data and to securely share that particular data with a data consumer system. In various embodiments described below, the data provider system is evaluated to determine what user data is stored at that system. Thereafter, a user may be presented with a description of their personal data, and a set of proposals that relate to proposed usages of that data by the data provider and/or one or more third parties. If the user provides assent to one or more of these proposals, the data provider system may initiate sharing of the specified portions of their data in a secure manner that is in accordance with the agreed-to proposals. These techniques may thus allow data ownership and custodianship of user data to be clarified, while permitting internal and external use of the data while complying with data usage policies of the data provider or a regulatory body or government.

This disclosure initially describes, with reference toFIGS.1-12, various techniques for discovering what data is stored at a data provider system and segmenting that data into different data segmentations that may be used to protect the data within those data segmentations. This disclosure then describes, with reference toFIGS.13-17, various techniques for implementing a data sharing architecture in which data may be shared with a data consumer system by a data provider system. The data sharing may include two distinct phases: a setup phase and a sharing phase. Finally, this disclosure describes, with reference toFIGS.18-22, various techniques that utilize techniques discussed with referenceFIGS.1-17to enable a data provider system to obtain authorization from a user to share particular data and to securely share that particular data with a data consumer system.

Turning now toFIG.1, a block diagram of a system100that incorporates multiple data-defined network systems140is depicted. In the illustrated embodiment, system100includes computing devices110, data stores111, network appliances120, and a firewall130. As further depicted, each network appliance120includes a DDN system140. While system100is shown as a single network of computing systems enclosed by a firewall, in some embodiments, system100expands across multiple networks that each have computing systems that are enclosed by their own respective firewalls. In some embodiments, system100is implemented differently than shown—e.g., system100may include DDN systems140, but not firewall130.

Managing data from the vantage point of the network perimeter is increasingly challenging, particularly with the current and expected further proliferation in governmental data usage regulations worldwide. To address such problems, the present disclosure sets forth a “data-defined” approach to data management. In this approach, data management problems can largely be seen as anomalous behavior of data, which can be addressed by classifying data in a network, defining “normal behavior” (or “anomalous behavior,” which refers to any improper use of data relative to some standard or data policy, whether or not that use is malicious), and then instituting an enforcement mechanism that ensures that anomalous data usage is controlled.

The current content and nature of data within a given computer network is typically poorly understood. Conventional infrastructure-driven approaches to network organization and data management are not concerned with what different types of data are present within a network and how that data normally behaves (whether that data is in use, at rest, or in transit), which puts such data management paradigms at a severe disadvantage when dealing with novel threats.

Data management broadly refers to the concept of ensuring that data is used in accordance with a policy objective. (Such “use” of the data includes the manner in which the data is stored, accessed, or moved.) The concept of data management thus includes data security (e.g., protecting data from malware attacks), data compliance (e.g., ensuring control of personal data is managed in accordance with a policy that may be established by a governmental organization), as well as permissioning that enforces entity-specific policies (e.g., certain groups in a company can access certain projects). The present disclosure describes a “data-defined” approach to data management, resulting in what is described as a “data-defined network” (DDN)—that is, a network (or portion of a network) that implements this data-defined approach.

Broadly speaking, a DDN stores one or more data structures in which data in a network is organized and managed on the basis of observed attributes of the data, rather than infrastructure-driven factors, such as the particular physical devices or locations where that data is stored. In this manner, a group of DDN data structures may form the building block of a DDN and incorporate multiple dimensions of relevant data attributes to facilitate capturing the commonality of data in a network. In some embodiments, a given one of the group of DDN data structures in a particular network may correspond to a set of data objects that have similar content (e.g., as defined by reference to some similarity metric) and indicate baseline behavior for that set of objects. As used herein, the term “observed behavior” refers to how data objects are observed to be used within a network; observed behavior may be determined through a learning or training phase as described in this disclosure. For example, if a document is exchanged between two computer systems, then exchanging that document between those two systems is said to be an example of observed behavior for that document.

When describing the behavior of data, the term “behavior” refers to actions performed on data, characteristics of those actions, and characteristics of those entities involved in the actions. Actions performed on the data may include without limitation reading, writing, deleting, transmitting, etc. Characteristics of those actions refers to properties of the actions being performed beyond the types of actions being performed on the data. Such characteristics may include without limitation the protocols used in those actions, the time when the action was initiated, the specific data involved in the action, parameters passed as part of the action, etc. Finally, data behavior also includes the identity and/or characteristics of the entities involved in the actions. Thus, if observed data behavior includes the transmission of data from user A to user B from a software entity C, data behavior can include information about user A, user B, and software entity C. Characteristics of the entities involved in the actions may include without limitation type of application transmitting the data, the type of system (e.g., client, server, etc.) running the application, etc. Accordingly, data behavior is intended to broadly encompass any information that can be extracted by a computer system when an operation is performed on a data object.

Once the observed behavior of a data object is determined, this information may be used to define the baseline behavior of the data object. The term “baseline behavior” (alternatively, “normal behavior” or “typical behavior”) refers to how a data object is expected to behave within a network, which, in many cases, is specified by observed behavior, as modified by any user-defined rules. Baseline behavior may thus be the observed behavior, or the observed behavior plus modifications specified by the user. Consider an example in which one observed behavior of a document is that the document is exchanged between three computer systems A, B, and C. The baseline behavior may be that the document can be exchanged between the three computer systems (which matches the observed behavior) or, because of user-intervention for example, the baseline behavior may also be that the document can be exchanged between computer systems A and B and D. When later evaluating data behavior, the term “anomalous behavior” refers to behavior of a data objects that deviates from the baseline behavior of that data object. A given DDN data structure may, in some cases, indicate policies for handling anomalous usage (e.g., preventing such usage or generating a message).

The organization of a DDN data structure that indicates content and behavior information is described herein as including a “content class” and a “behavior class.” This description is to be interpreted broadly to include any information that indicates a set of data objects that have similar content, in addition to baseline or typical behaviors for those data objects. As used herein, the term “class” refers to a collection of related information derived from a classification process. For example, a content class may identify individual data objects that are determined to be related to one another based on their content, and may further include features that describe the data objects and/or attributes of their content. A behavioral class may indicate a behavior of a data object, and may include specific behavioral features that define the associated behavior. These terms are not intended to be limited to any particular data structure format such as a “class” in certain object-oriented programming languages, but rather are intended to be interpreted more broadly.

In various embodiments that are described below, one or more DDN data structures are generated utilizing artificial intelligence (AI) algorithms (e.g., machine learning algorithms) to associate or link data objects having similar data content with their behavioral features and are then deployed to detect anomalous behavior and/or non-compliance with policy objectives. In various cases, the generation and deployment of DDN data structures may occur in two distinct operational phases.

During a learning phase, similarity detection and machine learning techniques may be used to associate data objects having similar data content and to identify the behavioral features of those data objects in order to generate a DDN data structure. In various embodiments, a user provides data object samples to be used in the learning phase. The data object samples that are provided by a user may be selected to achieve a particular purpose. In many cases, a user may select data object samples that have data that is deemed critical or important by the user. For example, a user may provide data object samples that have payroll information. Each of these samples may form the initial building block of a DDN data structure. After the data object samples have been received and processed, network traffic may be evaluated to extract data objects that may then be classified (e.g., using a similarity detection technique or a content classification model) in order to identify at least one of the data object samples with which the extracted data object shares similar content attributes. The content and behavioral features of that extracted data object may be collected and then provided to a set of AI algorithms to train the content classification model and a behavioral classification model. A DDN data structure, in various embodiments, is created to include a content class (of the content classification model) that corresponds to a sample and extracted data objects that are similar to that sample and to include one or more behavioral classes (of the behavioral classification model) that are associated with the behavioral features exhibited by those data objects.

During an enforcement phase, network traffic may be evaluated to extract data objects and to determine if those extracted data objects are behaving anomalously. In a similar manner to the learning phase, extracted data objects may be classified to determine if they fall within a content class of one of the DDN data structures—thus ascertaining whether they include content that is similar to previously classified content. In some embodiments, if a data object is associated with a content class, then its behavioral features are collected and classified in order to identify whether the current behavior of that data object falls within any of the behavioral classes associated with that content class. If the current behavior falls within one of the behavioral classes, then that data object can be said to exhibit normal or typical behavior; otherwise, that data object is behaving anomalously and thus a corrective action may be taken (e.g., prevent the data object from reaching its destination and log the event). In various cases, however, a data object may not comply with a policy objective and thus a corrective action may also be taken in such cases.

These techniques may be advantageous over prior approaches as these techniques allow for better data management based on a better understanding of the behavior of data. More specifically, in using these techniques, a baseline behavior may be identified for data objects (e.g., files) along with other information such as the relationships between those data objects. By understanding how a data object is routinely used, anomalous behavior may be more easily detected as the current behavior of a data object may be compared against how it is routinely used. This approach is distinct from, and complementary to, traditional perimeter-based solutions.

Because a DDN data structure may be used to identify data objects and their associated behavior and to enforce policy objectives against those data objects, this may enable a user to modify or refine the behavior of those data objects. As an example, subsequent to discovering a data management issue involving the misuse of certain data objects, a user may alter a policy to narrow the acceptable uses of those data objects. After mitigating a data management issue, a DDN data structure may adjust (or a new one may be generated) to identify the new baseline behavior of the data objects in view of the data management issue being mitigated. As such, a DDN data structure may continue to be used to track data behavior and identify any anomalous behavior, thereby helping to protect data from known and unknown data management issues.

Additionally, the techniques of the present disclosure may be used to discover previously unknown locations in a user's network where data of interest is stored. As such, a user may benefit from a greater insight into where data is located and/or the relationships that exist among data, users, applications, and/or networks that is provided by these techniques. As another example, users may be able to more easily comply with governmental regulations that attempt to control how certain data (e.g., PHI) should be handled because these techniques may establish the behavior of data and permit those users to conform that behavior in accordance with those governmental regulations. Various embodiments for implementing these techniques will now be discussed.

System100, in various embodiments, is a network of components that are implemented via hardware or a combination of hardware and software routines. As an example, system100may be a database center housing database servers, storage systems, network switches, routers, etc., all of which may comprise an internal network separate from external network105such as the Internet. In some embodiments, system100includes components that may be located in different geological areas and thus may comprise multiple networks. For example, system100may include multiple database centers located around the world. Broadly speaking, however, system100may include a subset or all of the components associated with a given entity (e.g., an individual, a company, an organization, etc.).

Computing devices110, in various embodiments, are devices that perform a wide range of tasks by executing arithmetic and logical operations (via computer programming). Examples of computing devices110may include, but are not limited to, desktops, laptops, smartphones, tablets, embedded systems, and server systems. While computing devices110are depicted as residing behind firewall130, a computing device110may be located outside firewall130(e.g., a user may access a data store111from their laptop using their home network) while still being considered part of system100. In various embodiments, computing devices110are configured to communicate with other computing devices110, data stores111, and devices that are located on external network105, for example. That communication may result in intra-network traffic115that is routed through network appliances120.

Network appliances120, in various embodiments, are networking systems that support the flow of intra-network traffic115among the components of system100, such as computing devices110and data stores111. Examples of network appliances120may include, but are not limited to, a network switch (e.g., a Top-of-Rack (TOR) switch, a core switch, etc.), a network router, and a load balancer. Since intra-network traffic115flows through network appliances120, they may serve as a deployment point for a DDN system140or at least portions of a DDN system140(e.g., an enforcement engine that determines whether to block intra-network traffic115). In various embodiments, network appliances120include a firewall application (and thus serve as a firewall130) and a DDN system140; however, they may include only a DDN system140.

Firewall130, in various embodiments, is a network security system that monitors and controls inbound and outbound network traffic based on predetermined security rules. Firewall130may establish, for example, a boundary between the internal network of system100and an untrusted external network, such as the Internet. During operation, in various cases, firewall130may filter the network traffic that passes between the internal network of system100and networks external to system100by dropping the network traffic that does not comply with the ruleset provided to firewall130. For example, if firewall130is designed to block telnet access, then firewall130will drop data packets destined to Transmission Control Protocol (TCP) port number23, which is used for telnet. While firewall130filters the network traffic passing into and out of system100, in many cases, firewall130provides no internal defense against attacks that have breached firewall130(i.e., have passed through firewall130without being detected by firewall130). Accordingly, in various embodiments, system100includes one or more DDN systems140that serve as part of an internal defense mechanism.

DDN systems140, in various embodiments, are data management systems that monitor and control the flow of network traffic (e.g., intra-network traffic115) and provide information that describes the behavior of data and its relationships with other data, applications, and users in order to assist users in better managing that data. As mentioned earlier, a DDN system140may use DDN data structures to group data objects that have similar content and to establish a baseline behavior for those data objects against which policies may be applied to modify the baseline behavior in some manner. The generation and deployment of a DDN data structure may occur in two operational phases.

In a learning phase, in various embodiments, a DDN system140(or a collection of DDN systems) learns the behavior of data objects by inspecting intra-network traffic115to gather information about the content and behaviors of data objects in traffic115and by training content and behavioral models utilizing that gathered information. Accordingly, through continued inspection of intra-network traffic115, baseline or typically behaviors of data objects may be learned, against which future intra-network traffic115observations may be evaluated to determine if they conform to the expected behavior, or instead represent anomalous behavior that might warrant protective action. The set of typical behaviors may be altered by a user such as a system administrator in some embodiments, resulting in an updated baseline set of operations permissible for a given group of data objects. That is, if a user finds that the typical behavior of a data object is undesirable, then the user may restrict that behavior by defining policies in some cases.

In an enforcement phase, in various embodiments, a DDN system140determines if a data object is exhibiting anomalous behavior by gathering information in a similar manner to the learning phase and by classifying that gathered information to determine whether that data object corresponds to a particular DDN data structure and whether its behavior is in line with the behavior baseline and the policy objectives identified by that DDN data structure. If there is a discrepancy between how the data object is being used and how it is expected to be used, then a DDN system140may perform a corrective action. It is noted that a data object may be determined to exhibit anomalous behavior based on either its content or its detected behavior attributes, or a combination of these. Anomalous behavior may include use of malicious content (e.g., a virus) as well as unexpected use of benign (but possibly sensitive) content. Thus, the techniques described herein can be used to detect content that should not be in the system, as well as content that is properly within the system, but is either in the wrong location or being used by users without proper permissions or in an improper manner.

By identifying a baseline behavior for a data object and then taking corrective actions (e.g., dropping that data object from intra-network traffic115) for anomalous behavior, a DDN system140may enforce policy objectives. For example, if malware is copying PHI records to an unauthorized remote server, a DDN system140can drop those records from intra-network traffic115upon determining that copying those records to that unauthorized remote server is not baseline behavior or in line with HIPPA policies, for example. Moreover, by continually observing data, a DDN system may provide users with an in-depth understanding of how their data is being used, where it is being stored, etc. With such knowledge, users may learn of other issues pertaining to how data is being used in system100and thus may be able to curtail those issues by providing new policies or altering old policies. The particulars of a DDN system140will now be discussed in greater detail below.

Turning now toFIG.2, a block diagram of an example DDN system140is shown. In the illustrated embodiment, DDN system140includes a data manager210, a data store220, and a DDN manager230. As shown, data manager210includes a data collection engine212and an enforcement engine214; data store220includes a DDN library222(which in turn has a set of DDN data structures225) and models227; and DDN manager230includes a learning engine235. While DDN systems140are shown as residing at network appliances120inFIG.1, some components of a DDN system140may reside at other locations—e.g., because learning engine235may not need to inspect intra-network traffic115, it may be located at a different place in system100. In some embodiments, DDN system140may be implemented differently than is shown—e.g., data manager210and DDN manager230may be the same component.

Data manager210, in various embodiments, is a set of software routines that monitors and controls the flow of data in intra-network traffic115. For example, data manager210may monitor intra-network traffic115for data objects that are behaving anomalously and drop the data objects from intra-network traffic115. To monitor and control the flow of data, in various embodiments, data manager210includes data collection engine212that identifies and collects the content and behavioral features (examples of which are discussed with respect toFIG.4) of data objects that correspond to data samples provided by users of DDN system140. (Such samples may be those types of data deemed important from the standpoint of an entity—for example, Social Security numbers or a user's private health information.) The content and behavioral features may then be stored in data store220for analysis by DDN manager230. Data collection engine212is described in greater detail below with respect toFIG.3.

Data store220, in various embodiments, is a repository that stores DDN data structures225and models227. In a sense, data store220may be considered a communication mechanism between data manager210and DDN manager230. As an example, the content and behavioral features extracted from data objects may be stored in data store220so that learning engine235may later use those features to train machine learning models227and to create a DDN library222of DDN data structures225. Moreover, enforcement engine214may retrieve models227and DDN data structures225from data store220in order to control the flow of intra-network traffic115.

DDN manager230, in various embodiments, is a set of software routines that facilitates the generation and maintenance of DDN data structures225. Accordingly, the features that are collected from data objects may be passed to learning engine235for training models227. For example, as described below, machine learning classification algorithms may be performed to classify data objects by their content, their behavior, or both. The content classes that are created, in various embodiments, are each included in (or indicated by) a respective DDN data structure225. Accordingly, when identifying a particular DDN data structure225to which a data object belongs, a general content model227may be used to classify the data object into a DDN data structure225based on its content class. The behavioral classes that are created, for a given behavioral model227(as there might, in some cases, be a behavioral model227for each DDN data structure225), may all be included in the same DDN data structure225. Thus, in various embodiments, a DDN data structure225includes a content class and one or more behavioral classes. The contents of a DDN data structure225are discussed in greater detail with respect toFIG.2and learning engine235is discussed in greater detail with respect toFIG.5.

After DDN data structures225are created and the behavior baselines are learned (and potentially updated by a user), for any data objects detected within intra-network traffic115, the content and behavioral features of that data object along with DDN data structures225may be pushed to enforcement engine214to detect possible anomalous behavior. The machine learning classification algorithms that were mentioned earlier may be performed on the content and behavioral features to ascertain if that data object is similar to established data objects (e.g., based on its content) and whether its behavior conforms to what is normal for those established data objects (e.g., in compliance with specified policy objectives), or what is instead anomalous.

In the discussions that follow, examples of how the learning phase is implemented are discussed (with an example of a learning workflow presented inFIG.6), followed by examples of how the enforcement phase is implemented (with an example of an enforcement workflow presented inFIG.8).

Turning now toFIG.3, a block diagram of an example data manager210and data store220in the learning phase are shown. In the illustrated embodiment, data manager210includes a data collection engine212, and data store220includes a DNN data structure225and models227. As further depicted, data collection engine212includes network scanner310and external scanner320. Also as shown, DDN data structure225includes a content class330, data objects335, behavioral classes340, behavioral features345, and user-defined policies350; models227include content classification model360and behavioral classification model370. In some embodiments, data manager210and/or data store220may be implemented differently than is shown—e.g., external scanner320may be omitted.

The learning phase, in various embodiments, starts with a user providing data samples305that the user identifies. In some cases, these may be types of data deemed important to a particular organization. Data samples305may include, for example, documents that contain PHI, business secrets, user information, and other personal information. By providing data samples305, the user may establish a baseline of the types of data that the user wishes to monitor and protect. That is, a user may not care, for example, about advertisements being improperly used, but may care about protecting Social Security numbers from being leaked and thus the user may provide data samples305in order to initially teach a DDN system140about the types of data that it should be monitoring and controlling.

Moreover, data samples305(which include content that user is aware of) may be used to discover similar or even the same content in locations that the user does not know store such content. For example, system100may store large amounts of unstructured data (e.g., PDFs, WORD documents, etc.) and thus files containing data that is relevant to the user may be buried in a directory that the user has forgotten about or did not know included this type of data. Accordingly, data samples305may be used to identify that a particular type of data is stored in previously unknown network locations. Furthermore, DDN data structures225(which may be built upon data samples305), in some embodiments, may be used to discover data exhibiting similar properties to the data samples. This approach may provide a user with knowledge about data that is similar to the data samples.

Users provide data samples305, in various embodiments, by granting access to the file storage (e.g., a network file system, a file transfer protocol server, or an application data store, each of which may be implemented by a data store111) where those samples (e.g., data objects335) are located. Data objects335may include files defined within a file system, which may be stored on storage systems (e.g., data stores111) that are internal to the network of system100, within the cloud (e.g., storage external to the network that may or may not be virtualized to appear as local storage), or in any other suitable manner. Although the following discussion refers to files, any type of data objects335may be employed, and it is not necessary that data objects335be defined within the context of a file system. Instead of granting access to a file storage, in some embodiments, users may directly upload data samples305to data manager210.

After accessing or receiving data samples305, data collection engine212may generate a respective root hash value337(also referred to as a “similarity hash value”) for one or more of the provided data samples305. In various embodiments, when generating a root hash value337, a data sample305is passed into a similarity algorithm that hashes that data sample using a piecewise hashing technique such as fuzzy hashing (or a rolling hash) to produce root hash values337. The piecewise hashing technique may produce similar hash values for data objects335that share similar content and thus may serve as a way to identify data objects335that are relatively similar. Accordingly, each root hash value337may represent or correspond to a set or group of data objects335. That is, each root hash value337may serve to identify the same and/or similar data objects335to a corresponding data sample305and may be used as a label for those data objects335(as illustrated) in order to group those data objects335with that data sample. In some embodiments, root hash values337are stored in data store220in association with their corresponding data sample305for later use. In some cases, data collection engine212may continuously monitor the provided data samples305, and update the root hash value337when a corresponding data sample305is updated.

Once root hash values337have been calculated for the provided data samples305, in various embodiments, data collection engine212may begin evaluating intra-network traffic115to identify data objects335that are similar to provided data samples305. In some embodiments, this data collection process used in the learning phase only monitors intra-network traffic115without actually modifying it. (For this reason, enforcement engine214has been omitted fromFIG.3). In contrast, the data collection process used in the enforcement phase may operate to discard or otherwise prevent the transmission of intra-network traffic115that is determined to exhibit anomalous behavior. (In some cases, the enforcement phase can include taking some other action other than discarding or preventing transmission of a data object.)

Network scanner310, in various embodiments, evaluates intra-network traffic115and attempts to reassemble the data packets into data objects335(e.g., files). Because data objects335are in transition to an endpoint that is assumedly going to use those data objects, network scanner310(and DNN system140as whole) may learn the behavioral features345(e.g., who uses those data objects, how often are they used, what types of applications request them, etc.) of those data objects. This approach provides greater visibility relative to only observing data objects335that are stored. For each data object335extracted from intra-network traffic115, network scanner310may generate a root hash value337(e.g., using a piecewise hashing technique). If the root hash value337matches any root hash value337of the provided data samples305(note that a root hash value337, in some embodiments, matches another root hash value337even if they are not exactly the same, but instead satisfy a similarity threshold (e.g., they are 80% the same root hash value337)) and thus the corresponding data object335is at least similar to one of the provided data samples305, then network scanner310, in various embodiments, extracts the content and behavioral features345of that data object335and stores that information in data store220. The content of that data object335(which may include a subset or all of a data object335) may be labeled with the matching root hash value337(as illustrated with data object335having a root hash value337) and associated with a content class330that may be labeled with the matching root hash value337. (Note that the relationship between data objects335and content class330is depicted by data objects335being within content class330, although data objects335are not necessarily stored in content class330. In other words, content class330may simply include an indication of what data objects335correspond to this class.)

In some cases, network scanner310may not be able to evaluate data objects335from intra-network traffic115as those data objects may be, for example, encrypted. It is noted that if a data object335is encrypted, then the piecewise hashing technique may not be effective in determining if that data object is similar to a data sample305. Accordingly, network scanner310may evaluate intra-network traffic115to identify, for data objects335in that traffic, where those data objects are stored (in addition to extracting their behavioral features345). Network scanner310may then cause external scanner320to obtain the appropriate credentials and scan the repository where those data objects are stored to determine if they contain information that is relevant to users of DDN system140. For example, if network scanner310extracts query results from intra-network traffic115that were sent by a MYSQL server, but the query results were encrypted by the MYSQL server, then external scanner320may be used to notify a user about the query results and to ask for access credentials so that it may scan the repository that is associated with that MYSQL server for relevant data. As shown, external scanner320may retrieve data325from locations where relevant data might be stored. Thus, external scanner320, in various embodiments, is used when network scanner310cannot fully understand the contents of data objects335.

While data objects335that have similar content to particular data samples305may be discovered by extracting them directly from intra-network traffic115, in various embodiments, network scanner310and external scanner320may identify locations where data objects335are stored and then scan those locations to determine if there are data objects335of interest. In order to identify these locations, network scanner310may first discover a data object335that has similar content to a data sample305and then may determine the location where that data object is stored. That location may be subsequently scanned by, e.g., external scanner320for other matching data objects335(e.g., by determining if their root hash value337matches one of the root hash values337for samples305). In some embodiments, users of DDN system140may direct data collection engine212to scan particular data repositories (e.g., data stores111). Thus, instead of reactively discovering data objects335that have desired information by extracting them from intra-network traffic115, data collection engine212may proactively find such data objects335by scanning data repositories. The content (e.g., data object335) obtained through external scanner320and behavioral features345obtained through network scanner310may be stored in data store220for later processing. This process of identifying locations and scanning the locations may assist in identifying areas where relevant data is stored that are unknown to users of DDN system140.

When a particular data object335matches a data object335(e.g., a data sample305) already in data store220and its contents and behavioral features345have been extracted, then those contents and behavioral features345may be processed for training content classification model360and behavioral classification model370, respectively. In various embodiments, this involves the application of unsupervised machine learning techniques to perform both content classification and identification of baseline behaviors of data objects335, as discussed in more detail below. After content classification model370has been trained, this model may assist (or be used in place of) the piecewise hashing technique to identify data objects335that have similar content to data objects335associated with DDN data structures225. For example, the piecewise hashing technique may not identify a desired data object335if that data is arranged or ordered in a significantly different manner than, e.g., data samples305. But content classification model360may still be able to identify that such a data object335includes data of interest (e.g., by using a natural language processing (NLP)-based approach). Content classification model360may further allow for different types of data objects335(e.g., PDFs versus WORD documents) to be classified.

Moreover, after a possible location of specified data has been determined (, in some embodiments, data collection engine212drives machine learning algorithms (that utilize an NLP-based content classification model360) to classify data objects335at that location to determine whether they correspond to a content class330of a DDN data structure225. If a data object335contains data of interest, then its behavioral features345may be used by machine learning algorithms to train behavioral classification model370as part of building a behavioral baseline. Before providing the content and behavioral features345of a data object335to data store220and/or DDN manager230, data collection engine212may normalize that information (e.g., by converting it into a text file). The normalized data object335may then be stored at data store220and a data ready message may be sent to the DDN manager230so that DDN manager may download that data object335and train content classification model360.

While the resulting classes (e.g., content classes330and behavioral classes340) from trained content and behavioral classifications models360and370, respectively, may form a portion of the DDN data structures225stored at data store220, a DDN data structure225may also include user-defined policies350. These user-defined policies350refer to user-supplied data that is used to supplement or modify the baseline set of behaviors set forth by model370—this may form a new baseline behavior. In some instances, user-defined policies350may be included with other policies that are derived (e.g., by a DDN system140) by translating model370into those other policies, which may be used to detect abnormal behavior.

As an example, consider a scenario in which model370records the transmission of PHI outside system100. A user-defined policy may remove this operation from the set of baseline behaviors that are permitted for the PHI. In this manner, a user-defined policy350may take an initial set of baseline behaviors from model370and produce a final set of baseline behaviors (which may of course be further altered as desired). Note that in some embodiments, the set of baseline behaviors as modified by user-defined policies350may all have an implicit action—for example, all baseline behaviors are permitted, and any non-baseline behavior is not permitted. In other embodiments, additional information may be associated with the set of baseline behaviors that specifies a particular action to be performed in response to a particular behavior.

As will be discussed below, because DDN system140collects the contents and behavioral features345of data objects335, DDN system140may provide users with an understanding of how data is being used along with other insightful information (e.g., the relationships between data objects335). A user may realize that certain data is being used in a manner that is not desirable to the user based on the baseline behavior exposed to the user by DDN system140. For example, a user may become aware that banking data is accessed by applications that should not have access to it. Accordingly, a user may provide a user-defined policy350that curtails the baseline behavior by preventing particular aspects of that behavior such as not allowing the banking data to be accessed by those applications that should not have access to it.

A DDN data structure225, in various embodiments, is built by a DDN system140to contain a content class330, behavioral classes340, and user-defined policies350that allow data to be managed in an effective manner. A DDN data structure225may be metadata that is maintained by a DDN system140. It is noted that a DDN data structure225is intended to not have any dependency on the underlying physical infrastructure built to store, transport or access data. Rather, it presents a logical view of all the data and their features for the same content class330. Examples of behavioral features345will now be discussed.

Turning now toFIG.4, a block diagram of example behavioral features345that might be collected for data objects335are shown. In the illustrated embodiment, behavioral features345include network traffic information410, application information420, device information430, API information440, and content features450. In some embodiments, other types of behavioral features may be collected in addition to the behavioral features345discussed below. All of these types of behavioral features need not be collected in all embodiments.

As explained earlier, a piecewise hashing algorithm and/or content classification model360may be used to identify data objects335(e.g., files) for further analysis. Once a data object335matches a root hash value337of, e.g., a data sample305or corresponds to a content class330, then that data object335itself (its contents) may be collected and then used for training content classification model360. But in addition to collecting the content of a data object335, behavioral features345related to that data object335may further be collected to help inform the expected behavior of that data object335. Any combination of the behavioral features345discussed below along with other features may be collected and stored with the content of a data object335for subsequent training of behavioral classification models370.

Network traffic information410, in various embodiments, includes information about the transmission of a data object335. When a data object335is extracted from intra-network traffic115, that data object335is nearly always in transit from some origin to some destination, either of which may or may not be within the boundary of system100. As such, the origin and destination of a data object335in transit may be collected as part of network traffic information410. Different protocols and applications may have different ways to define the origin and the destination and thus the information that is collected may vary. Examples of information that may be used to define the origin or the destination may include internet protocol (IP) addresses or other equivalent addressing schemes.

Information identifying any combination of the various open system interconnect (OSI) layer protocols associated with the transmission of a data object335may be collected as part of network traffic information410. As an example, whether a data object335is sent using the transmission control protocol (TCP) or the user datagram protocol (UDP) in the transport layer of the OSI model may be collected.

Application information420, in various embodiments, includes information about the particular application receiving and/or sending a data object335. For example, the information may include the name of an application and the type of the application. Moreover, a data object335may be routinely accessed by a certain group of applications that may share configuration parameters. Such parameters may be reflected in, for example, command-lines options and/or other application or protocol-related metadata that is conveyed along with a data object335in traffic115. These parameters may be collected to the extent that they can be identified.

An application associated with a data object335may be associated with a current data session that may be related to other network connections. When there are related sessions, the behavioral features345from the related sessions may further be collected, as they may inform the behavior of that data object. Within a given data session, there may be many queries and responses for access to a certain data object335. The frequency of access of that certain data object335over time may be collected as part of application information420. Related to access frequency, the volume of data throughput may also be collected since, for example, an anomaly in the volume of data transfer may be indicative of a data breach.

Device information430, in various embodiments, includes information about the agent or device requesting a data object335. Examples of such information may include whether the device is a server or a client system, its hardware and/or operating system configurations, and any other available system-specific information. In some instances, the particular data storage being accessed to transfer a data object335may present a known level of risk (e.g., as being accessible by a command and control server, and thus more vulnerable than storage accessible by a less privileged system, etc.). Accordingly, information regarding the level of security risk associated with data storage may be collected as part of device information430.

API information440, in various embodiments, includes information about application programming interfaces (API) that are used to access a data object335. As an example, a data object335may be accessed using the hypertext transfer protocol (HTTP) GET command, the file transfer protocol (FTP) GET command, or the server message block (SMB) read command and thus such information may be collected as part of API information440. An anomaly in the particular API calls or their sequence can be an indicator of a data breach. Accordingly, API sequence information may be collected as a behavioral feature345.

Content features450may include information that identifies properties of the content of a data object335. For example, for a WORD document, content features450may identify the length of the document (e.g., the number of words in the document), the key words used in the document, the language in which the document is written (e.g., English), the layout of the document (e.g., introduction→body→conclusion), etc. Content features450may also identify the type of a data object335(e.g., PDF, MP4, etc.), the size of a data object335(e.g., the size in bytes), whether a data object335is in an encrypted format, etc. Content features450, in various embodiments, are used to detect abnormal behavior. For example, if a data object335is normally in an unencrypted format, then obtaining a content feature450that indicates that the data object335is in an encrypted format may be an indication of abnormal behavior. In some embodiments, content features450may be used to train a content classification model360and to determine to which content class330that a data object335belongs.

It is noted that not all of the aforementioned features345are necessarily used together in each embodiment. In some embodiments, the particular features345that are collected may be dynamically altered during system operation, e.g., by removing some features and/or adding others. The particulars of one embodiment of DDN manager230will now be discussed with respect toFIG.5.

Turning now toFIG.5, a block diagram of an example DDN manager230is shown. In the illustrated embodiment, DDN manager230includes a learning engine235(having machine learning and deep learning algorithms510) and a user interface520. In some embodiments, a DDN manager230may be implemented differently than shown—e.g., user interface520may be separate from DDN manager230.

As explained earlier, to collect data for machine learning training purposes, a piecewise hashing algorithm may initially be used to discover, based on evaluating intra-network traffic115, data objects335with content similar to provided data samples305. Under this approach, the assumption is that data objects335sharing enough content similarity should be in the same content class330. The piecewise hashing algorithm may be further assisted, however, by using machine learning content classification methods to help identify more data objects335that are similar to provided data samples305. As an example, machine learning content classification may facilitate similarity detection in cases that are difficult for the piecewise hashing algorithm to handle such as content that is contextually the same, but is ordered in a reasonably different manner than the provided data samples305. It is noted, however, that in various embodiments, machine learning content classification may be omitted (e.g., in the cases where the piecewise hashing algorithm provides sufficient coverage and accuracy).

Learning engine235, in various embodiments, trains content classification models360using machine learning and deep learning algorithms510. For example, learning engine235, in some embodiments, uses algorithms510such as support vector machine (SVM) algorithms and convolutional neural network (CNN) algorithms to train content classification models360such as a set of SVM models in conjunction with a set of CNN models, although many other architectures that use different algorithms510are possible and contemplated. Root hash values337(discussed above) may serve as labels for the content classes330that result from content classification models360.

In some embodiments, learning engine235uses machine learning and deep learning algorithms510to identify specific types of data objects335and to generate pattern matching rules (e.g., regex expressions) or models that may be used on a specific type of data object335to identify whether that data object335includes data of interest. More specifically, discovering information of interest (e.g., PHI) in different types of unstructured data (e.g., PDFs, pictures, etc.) may be challenging for, e.g., a piecewise hashing algorithm. Accordingly, learning engine235may train a set of natural language processing (NLP) content classification models (which are examples of content classification models360) to classify a data object335to determine if that data object335is part of a content class330. If that data object335belongs to a content class330within DDN system140, then pattern matching rules (which may be generated using algorithms510) may be used on that data object335to extract any information of interest. For example, content classification models360may classify a credit card PDF form as belonging to a PII content class330and thus regular expressions (which may be selected specific to PDFs) may be used to identify whatever PII is in that credit card PDF form.

Learning engine235, in various embodiments, further trains behavioral classification models370using machine learning and deep learning algorithms510. For example, learning engine235, in some embodiments, uses algorithms510such as convolutional neural network (CNN) algorithms and recurrent neural networks (RNN) algorithms to train behavioral classification model370such as a set of CCN models in conjunction with a set of RNN models, although many other architectures that use different algorithms510are possible and contemplated. In some cases, RNN models may be used for tracking time series behavior (e.g., temporal sequences of events) while CNN models may be used for classifying behavior that is not time-dependent. Behavioral class340, in some embodiments, are labeled with a unique identifier and associated with a content class330. Accordingly, a single content class330may be associated with a set of behavioral classes340. Together, a content class330and behavioral classes340may define the behavioral benchmark of a data object335(i.e., the baseline behavior, which may be based on the observed behavior of that data object335within intra-network traffic115).

Thus, the collected content and behavioral features345may be used by learning engine235for training content classification models360and behavioral classification models370to perform content and behavioral classification, respectively. The process of classification may result in classes, such as content classes330and behavioral classes340. It is noted, however, that although machine learning classification techniques may be used to generate classes, any suitable classification technique may be employed.

When machine learning classification training is complete, in various embodiments, the resulting models227may be deployed for real-time enforcement, either in the network device that completed the learning phase, or in other devices within the network. As an example, models227may be packed into Python objects and pushed to data manager210that can perform real-time enforcement (e.g., which, as discussed earlier, may be situated within a network appliance120in such a manner that it may intercept anomalous traffic and preventing it from being further transmitted within the network of system100). In order to support real-time enforcement, in various embodiments, DDN data structures225are provided to data manager210.

User interface520, in various embodiments, provides information maintained by DDN system140to users for better understanding their data. That information may include the data objects335, content classes330, behavioral features345, behavioral classes340, and policies350of DDN data structures225maintained at data store220in addition to models227. Thus, interface520, in various embodiments, issues different query commands to the data stores220to collect information and present DDN data structure225details to users. DDN data structure225information may be presented to users in a variety of ways.

User interface520may provide users with access and history information (e.g., users, their roles, their location, the infrastructure used, the actions performed, etc.). This information may be presented in, e.g., tables, graphs, or maps, and may indicate whether an access involves one DDN data structure225or multiple difference DDN data structures225. This information may, in various cases, be based on collected behavioral features345.

User interface520may provide users with content information that presents a measure of distance (or similarity) between different data objects335. For example, two different data objects335may have a certain level of content similarity (e.g., 80% similar), but have different behavioral features345. By viewing content information in this manner, users may be enabled to evaluate related DDN data structures225and modify data usage patterns. For example, if two data objects335are quite similar in content but have divergent behaviors, administrators may intervene to change the data access structure (e.g., by changing rules or policies350) to bring those data objects into better conformance, which may help improve performance and/or security, for example.

User interface520may provide users with data dependency information that presents the data dependencies among various objects (e.g., in order to display a web page, the database record x in table z needs to be accessed). This dependency information may span across DDN data structures225, creating a content dependency relationship between them. If an anomaly is detected with respect to one DDN data structure225, dependency information may facilitate determination of the potential scope of that anomaly. For example, if the data objects335that are associated with a DDN data structure225are to be isolated after detection of an anomaly, then dependency information may facilitate determining how widespread the impact of such isolation might be. The dependency information may be part of the behavioral information that is collected for a data object335. For example, a data object335may be observed on multiple occasions to be in transit with another object335or may be observed in response to particular requests that are extracted from network traffic. Accordingly, the behavior of that data object335may indicate that it depends on that other data object335or that the object depends on it. Also, when investigating an actual attack or malicious event, considering the lateral impact may be more comprehensively performed from a content or even application dependency level than from just the network level. This information may also be extended to include application dependencies (e.g., application A uses data C that has a content dependency on data D that is also created/managed by application B).

User interface520may provide users with security information, such as information regarding security best practices for certain types of data and the status of security compliance of various data objects335. User interface520may also provide users with user-defined rule information. As noted elsewhere, users may provide their own policies350used for similarity detection, content classification, behavioral classification, and enforcement. Accordingly, user interface520may enable users to view, change, and create rules

Thus, user interface520may provide users with a better understanding of their data, and based on that understanding, allow them to improve their data protection and optimize data usage. Particularly, it may help users to construct a data usage flow across different DDN data structures225, and map these into user-defined business intents—enabling a user to evaluate how data is being used at various steps of the flow, and whether those steps present security risks. An example learning workflow will now be discussed.

Turning now toFIG.6, a block diagram of an example learning workflow600is shown. In the illustrated embodiment, learning workflow600involves a data manager210, a data store220, and a DDN data structure225. As shown, the illustrated embodiment includes numerical markers indicating one possible ordering of the steps of learning workflow600.

As illustrated, data samples305, in various embodiments, are initially provided to data manager210(e.g., by a user of DDN system140). Those data samples305may be copied to a local or external storage that is accessible to data manager210or may be directly uploaded to data manager210. Once data samples305have been obtained, in various embodiments, data manger210uses a piecewise hashing algorithm (as explained earlier) to generate a root hash value337for each of the provided data samples305, and then stores those root hash values337along with those data samples in data store220.

Thereafter, data manager210may begin monitoring intra-network traffic115and may extract a data object335from that traffic. Accordingly, in various embodiments, data manager210normalizes that data object335, generates a root hash value337for it, and compares the generated root hash value337with the root hash values337associated with the provided data samples305. If the generated root hash value337meets some specified matching criteria (e.g., 80% correspondence) for a root hash value337of a data sample305, then data manager210may store the corresponding data object335and its behavioral features345in association with the same set as the matching data sample305. In some instances, that data object335and its behavioral features345may be labeled with the root hash value337of the relevant data sample305.

The data object335and its behavioral features345, in various embodiments, are passed through DDN manager230in order to create a DDN data structure225and thus, to create the initial baseline behavior for that data object335. If a DDN data structure225already exists for the group corresponding to that data object335, then the DDN data structure225and models227may also be retrieved and trained using that data object335and its behavioral features345. In various embodiments, once a DDN data structure225and models227are created or updated, DDN manager230stores them in data store220. Thereafter, data manager210may retrieve the DDN structure225and models227to be used for future learning or enforcement. As discussed, the initial baseline behavior set for a data object may be modified by user-defined policies in order to create an updated baseline behavior set.

Accordingly, once sufficient information has been collected during the learning phase, the enforcement may be enabled. (In some embodiments, the learning phase may continue to operate during enforcement, enabling enforcement to dynamically adapt to data behavior over time.)

As shown inFIG.1, system100may include multiple DDN systems140, each of which may implement the learning phase as discussed above. In some cases, the information obtained by one DDN system140during its learning phase may be passed to another DDN system140for use. As an example, a DDN data structure225generated by one DDN system140may be provided to another DDN system140to be used during its enforcement phase. In this manner, the learning performed by one DDN system140augment the learning of another DDN system140. Moreover, the learning phases between DDN systems140may be different. For example, one DDN system140may receive a user-defined policy350that is different than one received by another DDN system140. Particular embodiments of the enforcement phase based on data created and modified in the learning phase will be discussed next.

Turning now toFIG.7, a block diagram of an example data manager210implementing an enforcement phase is shown. In the illustrated embodiment, data manager210includes data collection engine212and enforcement engine214. As further shown, enforcement engine214includes an enforcer module710and a log720. For illustrative purposes, two different types of intra-network traffic are depicted: intra-network traffic115A that is normal (i.e., expected or permissible) and intra-network traffic115B that exhibits anomalous or unwanted behavior. In some embodiments, data manager210may be implemented differently than shown—e.g., enforcement engine214may not include log720.

Similar to the learning phase, in various embodiments, the enforcement phase involves collecting content and behavioral features345from the data objects335that are extracted from intra-network traffic115. Accordingly, as shown, intra-network traffic115may pass through data collection engine212so that content and behavioral features345can be collected before that traffic passes through enforcement engine214. The content and/or behavioral features345that are collected may be provided to enforcer module710for further analysis. In some embodiments, behavioral features345collected for enforcement may be the same as those features collected for the learning phase, although in other embodiments the features may differ.

Enforcer module710, in various embodiments, monitors and controls the flow of intra-network traffic115(e.g., by permitting data objects335to pass or dropping them) based on user-defined policies350. Accordingly, enforcer module710may obtain DDN data structures225and models227from data store220and use them to control traffic flow. In various embodiments, content and behavioral features345are classified using models227that were trained in the learning phase into a content class330and a behavioral class340, respectively, in order to determine whether the corresponding data object335is associated with normal or anomalous behavior. Enforcer module710may first classify a data object335, based on its content, into a content class330in order to determine whether that data object335belongs to a particular DDN data structure225. If a data object335falls into a content class330that is not associated with any DDN data structure225, then it may be assumed that the data object335does not include content that is of interest to the users of DDN system140and thus the data object335may be allowed to be transmitted its destination, but may also be logged in log720for analytical purposes. But if a data object335falls into a content class330that is associated with a certain DDN data structure225, then its behavioral features345may be classified. As such, behavioral classification in some embodiments may be performed only on data objects335identified during content classification. In other embodiments, however, it is contemplated that content and behavioral classification may occur concurrently. Moreover, in yet some embodiments, enforcement decisions may be made solely on the basis of behavioral classification.

Behavioral features345, in various embodiments, are classified by using the behavioral classification model370, which may then produce a behavioral classification output, e.g., in the form of a list of behavior class scores. If the classification of the behavioral features345of the data object335falls into a behavioral class340of the corresponding DDN data structure225, then the behavior of that data object335may be deemed normal and the data object335may be allowed to pass, but a record may be stored in log720. If, however, the classification does not fall into any behavioral classes340of the corresponding DDN data structure225(i.e., the DDN data structure225that the data object335belongs to by virtue of its content being classified into the content class330of that DDN data structure225), then the behavior of the data object335may be deemed anomalous and a corrective action may be taken. In various embodiments, a data object335exhibiting anomalous behavior is dropped from intra-network traffic115(as illustrated by intra-network traffic115B not passing beyond enforcer module710) and a record is committed to log720. Log720, in various embodiments, records activity pertaining to whether data objects335are allowed to pass or dropped from traffic and can be reviewed by users of DDN system140.

User-defined policies350, in various embodiments, may permit the behavior of a data object335to be narrowed or broadened. For example, even if a data object is not indicated to be anomalous based on the content and/or behavioral classifications, it may fail to satisfy one or more user-defined policies350, and may consequently be identified as anomalous. Such a data object335may be handled in the same manner as data objects335that otherwise fail the machine learning classification process, or it may be handled in a user-defined fashion. For example, if a data object335has been regularly used by a group of users and an administrator learns of this behavior via DDN system140and updates a policy350preventing that group of users from using that data object335, then when that data object335is classified by enforcer module710, it will still appear to be behaving normally. Enforcer module710, however, may drop the data object335from intra-network traffic115because of a policy350(and/or a policy derived by a DDN system140based on behavioral features345).

Thus, in various embodiments, using content and behavioral classification results along with policies350, enforcer module710can verify if a data object335has the desired behavior and/or content. If the results of classification or policies350indicate that the data object is anomalous (either with respect to its content or its behavior, or both) further transmission of the data object will be prevented (e.g., by discarding or otherwise interdicting the traffic associated with that data object335).

In some embodiments, in order to enable consistent data management at different areas of system100, the data (e.g., DDN data structure225and models227) maintained at data store220may be spread around to different components of system100(e.g., copies may be sent to each DDN system140in system100). Accordingly, enforcers710at different areas in system100may each monitor and control intra-network traffic115using the same DDN information; however, in some cases, each DDN system140may maintain variations of that information or its own DDN information. As an example, a DDN system140that receives traffic from a data store111that stores PHI and PII may monitor that traffic for those types of information while another DDN system140in the same system100that receives traffic from another data store111that stores PII and confidential information may monitor that traffic for those types. These DDN systems140, however, may in some cases share DDN information relevant to controlling PII since they both monitor and control that type of information.

In various embodiments, data-based segmentation may be used in which logical perimeters are built around data of interest to protect that data in many cases. These perimeters allow for policies to be employed against that data. Enforcer modules710may, in some cases, be deployed at locations near data of interest and ensure that anomalous use of that data (e.g., the data is not being used in accordance with a particular policy350and/or a policy that may be derived from behavioral classification model370) is prevented. For example, a user may wish to protect Social Security numbers. Accordingly, using DDN data structures225and enforcer modules710, a logical, protective perimeter may be established around areas where Social Security numbers are stored, despite those numbers possibly being stored within different data stores that are remote to each other. The user may define a set of policies350that are distributed to the enforcer modules710for preventing behavior that is not desired by the user. In various embodiments, DDN information (e.g., DDN data structures225) may be shared between enforcer modules710that are protecting the same data of interest. Data-based segmentation is discussed in greater detail with respect toFIG.12. An example enforcement workflow will now be discussed.

Turning now toFIG.8, a block diagram of an example enforcement workflow800is shown. In the illustrated embodiment, enforcement workflow800involves a data manager210and a data store220. As shown, the illustrated embodiment includes numerical markers that indicate one possible ordering of the steps of enforcement workflow800.

As illustrated, data manager210, in various embodiments, initially retrieves DDN data structures225and models227from data store220. Thereafter, data manager210may monitor intra-network traffic115and may extract a data object335from that traffic115. As such, data manager210, in some embodiments, classifies that data object335using content classification model360into a content class330. That content class330may then be used determine if the data object335falls into a content class330associated with a DDN data structure225. If not, then that data object335may be allowed to reach its destination; otherwise, data manager210, in some embodiments, classifies that data object335using behavioral classification model370into a behavioral class340. That behavioral class340may then be used to determine if the data object335falls into a behavioral class340that is corresponds to the content class330in which the data object335has been classified. If it does, then one or more policies350may be applied to that data object335and if it satisfies those policies, then it may be allowed to pass. But if the data object's behavioral class340does not match behavioral class340in the corresponding DDN data structure225, then, in various embodiments, it is prevented from passing (e.g., it is dropped from intra-network traffic115) and the incident is recorded in log720.

Similar to the learning phase, information gathered during the enforcement phase may be shared between DDN systems140. In various instances, a particular DDN system140may be responsible for monitoring and controlling a particular type of data (e.g., PHI) while another DDN system140may be responsible for monitoring and controlling a different type of data (e.g., PII). Moreover, in some embodiments, a system100may employ DDN systems140that implement different roles (e.g., one may implement the learning phase while another may only implement the enforcement phase). As such, those DDN system140may communicate data between each other to help each other implement their own respective roles.

Turning now toFIG.9, a flow diagram of a method900is shown. Method900is one embodiment of a method performed by a computer system (e.g., DDN system140) to control data within a computing network (e.g., network of system100). In some embodiments, method900may include additional steps—e.g., the computer system may present a user interface (e.g., user interface520) to a user for configuring different aspects (e.g., user-defined policies350) of the computer system.

Method900begins in step910with the computer system evaluating network traffic (e.g., intra-network traffic115) to extract and group data objects (e.g., data objects335) based on their content satisfying a set of similarity criteria, and to identify baseline data behavior with respect to the data objects. In some embodiments, the computer system receives one or more user-provided data samples (e.g., data samples305), generates respective root hash values (e.g., root hash values337) corresponding to the one or more user-provided data samples, and then stores the root hash values in a database (e.g., data store220). Accordingly, the computer system may determine that the content of a given one of the data objects satisfies the set of similarity criteria by generating a data object hash value of the given data object and then by determining that the data object hash value matches a given one of the root hash values stored in the database. In some embodiments, subsequent to determining that a given one of the one or more data objects satisfies the set of similarity criteria, the computer system stores a record of behavioral features (e.g., behavioral features345) associated with the given data object.

In step920, the computer system generates a set of data-defined network (DDN) data structures (e.g., DDN library222of DDN data structures225) that logically group data objects independent of physical infrastructure via which those data objects are stored, communicated, or utilized. A given one of the set of DDN data structures may include a content class (e.g., content class330) and one or more behavioral classes (e.g., behavioral classes340). The content class may be indicative of one or more of the data objects that have been grouped based on the one or more data objects satisfying the set of similarity criteria and the one or more behavioral classes may indicate baseline network behavior of the one or more data objects within the content class as determined from evaluation of the network traffic. In some embodiments, the content class of a given DDN data structure may be based upon a machine learning content classification of content of a given data object. In some embodiments, the one or more behavioral classes of the given DDN data structure may be based upon a machine learning behavioral classification the record of behavioral features associated with the given data object. The machine learning behavioral classification may involve training a set of convolutional neural networks (CNN) and recurrent neural networks (RNN) using the record of behavioral features associated with the given data object. In some cases, other networks may be used instead of CNN and RNN, such as long short-term memory (LSTM) networks.

In step930, the computer system detects anomalous data behavior within network traffic based on the content classes and the behavioral classes of the generated set of DDN data structures. In some embodiments, the computer system may detect anomalous data behavior by identifying an extracted data object from the network traffic and evaluating the extracted data object with respect to the content class and the one or more behavioral classes of ones of the DDN data structures. Such an evaluation may include determining, based upon the machine learning behavioral classification, that the extracted data object does not exhibits expected behavior and then indicating that the extracted data object exhibits anomalous behavior based upon the extracted data object failing to exhibit the expected behavior.

In step940, in response to detecting the anomalous data behavior, the computer system prevents network traffic corresponding to the anomalous data behavior from being communicated via the computing network.

Turning now toFIG.10, a flow diagram of a method1000is shown. Method1000is one embodiment of a method performed by a computer system (e.g., DDN system140) to manage data. Method1000may, in some instances, be performed by executing a set of program instructions stored on a non-transitory computer-readable medium. In some embodiments, method1000may include additional steps—e.g., the computer system may present a user interface (e.g., user interface520) to a user for configuring different aspects (e.g., user-defined policies350) of the computer system.

Method1000begins in step1010with the computer system evaluating network traffic (e.g., intra-network traffic115) within a computing network (e.g., a network across multiple systems100) to group data objects (e.g., data objects335) based on their content satisfying a set of similarity criteria, and to identify baseline network behavior with respect to the data objects. In some embodiments, the computer system retrieves a plurality of data samples (e.g., data samples305) from one or more storage devices, generates a respective plurality of root hash values (e.g., root hash values337) using the plurality of data samples; and then stores the plurality of root hash values within a database (e.g., data store220). Accordingly, determining that content of a given one of the data objects satisfies the set of similarity criteria may include generating a data object hash value for the given data object and then determining that the data object hash value matches a given one of the root hash values stored in the database.

In step1020, the computer system generates a data structure (e.g., DDN data structure225) that includes a content class (e.g., content class330) based on machine learning content classification and one or more behavioral classes (e.g., behavioral classes340) based on machine learning behavioral classification. The content class may be indicative of one or more of the data objects that have been grouped based on the one or more data objects having a set of similar content and the one or more behavioral classes may be indicative of baseline network behavior of the one or more data objects within the content class as determined from evaluation of the network traffic.

In step1030, the computer system detects anomalous data behavior within network traffic utilizing the data structure. Detecting anomalous data behavior may include identifying an extracted data object from the network traffic and evaluating the extracted data object with respect to the content class and the one or more behavioral classes of the data structure. In some cases, evaluating the extracted data object with respect to the content class and the one or more behavioral classes of the data structure may further comprise: determining, based upon the machine learning behavioral classification, that the extracted data object does not exhibits expected behavior; and indicating that the extracted data object exhibits anomalous behavior based upon the extracted data object failing to exhibit the expected behavior. In some instances, the computer system may obtain one or more user-defined rules (e.g., user-defined policies350) regarding content or behavior of data objects and may store the one or more user-defined rules in association with the data structure. Accordingly, evaluating the extracted data object with respect to the content class and the one or more behavioral classes of the data structure may further comprise: determining, based upon the machine learning behavioral classification, that the extracted data object exhibits expected behavior; and in response to determining that the extracted data exhibits expected behavior, determining that the extracted data object fails to satisfy the one or more user-defined rules included in the data structure; and indicating that the extracted data object exhibits anomalous behavior based upon the extracted data object failing to satisfy the one or more of the user-defined rules.

In step1040, in response to detecting the anomalous data behavior, the computer system prevents the network traffic corresponding to the anomalous data behavior from being communicated via the computing network.

Turning now toFIG.11, a flow diagram of a method1100is shown. Method1100is one embodiment of a method performed by a computer system (e.g., a network appliance120) to manage data. The computer system may include a plurality of network ports configured to communicate packetized network traffic, one or more processors configured to route the packetized network traffic among the plurality of network ports; and a memory that stores program instructions executable by the one or more processors to perform method1100. The computer system may be a network switch or a network router. In some embodiments, method1100includes additional steps such as implementing a firewall (e.g., firewall130) that prevents network traffic from being transmitted to a device coupled to the network appliance based on that network traffic failing to satisfy one or more port-based rules.

Method1100begins in step1110with the computer system evaluating packetized network traffic (e.g., intra-network traffic115) to identify data objects (e.g., data objects335) that satisfy a set of similarity criteria with respect to one or more user-provided data samples (e.g., data samples305). Determining that a given one of the set of data objects satisfies the set of similarity criteria may comprise generating a data object hash value (e.g., root hash value337) of the given data object and determining that the data object hash value matches a given root hash value stored in a database, which may store one or more root hash values respectively generated from one or more user-provided data samples.

In step1120, in response to identifying a set of data objects that satisfy the set of similarity criteria, the computer system stores content and behavioral features (e.g., behavioral features345) associated with the set of data objects in a database.

In step1130, the computer system generates a plurality of data-defined network (DDN) data structures (e.g., DDN data structures225) based on the stored content and behavioral features associated with the set of data objects. A given one of the plurality of DDN data structures may include a content class (e.g., content class330) and one or more behavioral classes (e.g., behavioral classes340). The content class may be indicative of one or more of the set of data objects that have been grouped based on the one or more data objects having a set of similar content. The one or more behavioral classes may indicate baseline network behavior of the one or more data objects within the content class as determined from evaluation of the network traffic.

In step1140, the computer system detects, using content and behavioral classes of the plurality of DDN data structures, anomalous data behavior within network traffic. Detecting anomalous data behavior within network traffic based upon the plurality of DDN data structures may comprise: (1) identifying an extracted data object and one or more behavioral features associated with the extracted data object from network traffic and (2) evaluating the extracted data object with respect to a content class and one or more behavioral classes of one of the plurality of DDN data structures. Determining that the extracted data object exhibits anomalous behavior may be based upon a machine learning content classification indicating that the content of the extracted data object differs from expected content.

In step1150, the computer system prevents the network traffic corresponding to the anomalous data behavior from being transmitted to a device coupled to the network appliance.

An example use case for the techniques discussed above is presented here. It is noted that this use case is merely an example subject to numerous variations in implementation.

Many organizations have sensitive data that has a long shelf life. This data is usually formatted as structured files and stored in a local storage or in the cloud. Such files are often downloaded, accessed, and shared among the employees of the organization or sometimes with entities outside of the organization. Accordingly, it may be desirable to track the use of those files and ensure that they are handled correctly.

DDN system140may provide a data management solution that utilizes unsupervised machine learning to learn about data objects335and their behavioral features345. By doing that, DDN system140may help businesses to continuously discover sensitive data usage inside their organizations, discover misuse of that sensitive data, and prevent data leakage caused by, e.g., an intentional attack or unintended misuse.

As described above, a DDN system140may learn about the sensitive data usage inside a customer's network environment by analyzing a set of data samples305and then continuing to discover the data usage and time series data updates inside the customer's networks by using a piecewise hashing algorithm or a content classification model360. While new data is being discovered, DDN system140may continue to learn the usage behavior of the data through the machine learning models. Once the data use behaviors are identified, DDN system140may provide the protection to the sensitive data, by detecting and intercepting anomalous network traffic. The DDN architecture described above may facilitate the decoupling of data tracking and protection functions from underlying network infrastructure and further allow continuing protection of data while the underlying network infrastructure is changing.

Inside an enterprise, there are typically records of PII or sensitive personal information (SPI), e.g., of employees and customers. Such information may include, for example, address and phone number information, Social Security numbers, banking information, etc. Usually, records of this type of information are created in enterprise data storage when the customer or employee initially associates with the enterprise, although it could be created or updated at any time during the business relationship. PII/SPI-based records are normally shared by a number of different enterprise applications (e.g., Zendesk, Workday, other types of customer analytics systems or customer relationship management systems) and may be stored inside plain text files, databases, unstructured big data records, or other types of storage across the on-premise file systems or in cloud storage.

Accordingly, a DDN system140may classify the PII/SPI data objects335into DDN data structures225based on the observed data usage behavior. This can enable enterprise users to gain deep visibility into their PII/SPI data usage. The DDN data structures225, along with other system140features such as user interface520, may assist users in identifying PII/SPI data that may be improperly stored or used, to measure data privacy risk, to verify regulatory compliance, and to learn data relationships across data stores111. DDN system140may continually refine the PII/SPI data usage behavior benchmark based on unsupervised machine learning models (e.g., models227). Once an accurate behavioral benchmark is established, the enforcement workflow may help customers to control and protect the PII/SPI data from misuse and malicious accesses.

Turning now toFIG.12, a block diagram depicting example data-based segmentations of a system100. In the illustrated embodiment, system100includes data stores111A-D, data managers210A-D, and a DDN manager230. As further illustrated, a data segmentation1220A includes data1210A maintained in data stores111A and111B, and a data segmentation1220B includes data1210B maintained in data stores111A,111C, and111D. Data1210A and1210B may include various data objects335that may be used to build DDN data structures225. Also as illustrated, data managers210A and210B include DDN data structure225A and models227, and data managers210A,210C, and210D include DDN data structure225B and models227. In some embodiments, system100may be implemented differently than shown. As an example, data stores111A-D may include different data1210than shown.

As mentioned earlier, the various techniques discussed in the present disclosure may be used for implementing data-based segmentation. For the sake of context, a small amount of background information about the general concept of segmentation may be useful. Many large-scale systems (or even single computer systems) include a firewall that implements a defensive perimeter around the entire system. The firewall aims to prevent malicious attacks originating from outside a system from affecting the system; however, once an attack breaches the firewall, the firewall may then be ineffective to contain the attack internally. That is, a malicious virus, for example, may move unopposed through the various systems within a large-scale system once it has passed through the firewall that protects the large-scale system.

Some individuals have turned to implementing segmentation-based concepts in which internal perimeters are built around portions of a system that protect those portions from other portions of the same system. By segmenting a system into different portions, a second layer of protection is built that can protect the system even when the firewall fails. A well-known form of segmentation is network-based segmentation. In a very traditional large-scale system, most or all of the servers and workstations of the large-scale system were located on the same local area network. This, however, allowed for a malicious actor such as malware to pivot from one system to another fairly easily. To help resolve this security issue, servers and/or workstations were segmented by physically or virtually locating those systems on different networks. As an example, servers that handle financial transactions may be located on one virtual network while servers that handle website requests may be located on another virtual network. Accordingly, if a malicious actor successfully infiltrated the virtual network of the website servers, the actor may not be able to infiltrate the virtual network of the financial servers as it can neither see nor reach the financial servers from the former virtual network. Thus, network-based segmentation allows for a second layer of protection by segmenting the internal components of a system into different networks.

Network-based segmentation (and the other known forms of segmentation), however, has drawbacks. For example, network-based segmentation suffers from scalability issues that occur when increasing the numbers of servers of a system as the restructuring of the physical connections to accommodate new servers can be overly burdensome. Accordingly, it may be desirable to perform segmentation in a way that overcomes some or all of the downsides of the currently known forms of segmentation.

The various techniques discussed in the present disclosure may be used to implement data-based segmentation in which logical perimeters are built based on and around data. Such perimeters may serve to protect data (e.g., data objects335in data1210A or1210B) from malicious attacks or unintentional misuses (for example, use of personal data that would contravene governmental privacy regulations or company policies). In contrast to the network-based segmentation where systems are segmented by placing them on different networks, data-based segmentation, in various embodiments, segments data by using DDN data structures225(which may include protection policies discussed below) and models227to manage access to data. In order to accomplish this, in some embodiments, data managers210are instantiated in logical proximity to data such as by being hosted on the same hypervisor as, for example, a database server that manages requests for data. Accordingly, a data manager210may monitor network traffic in/out of the hypervisor and detect abnormal use of data based on DDN data structures225and models227that have been pushed to that data manager by a DDN manager230. In various cases, the logical perimeters that are built around particular data may be independent of the physical infrastructure that stores that data. As shown for example, data segmentation1220A is built around data1210A stored at different data stores111(which may be different physical storage drives that are associated with different networks).

The process for building a data segmentation1220may start, in various embodiments, with the learning phase/workflow explained earlier. Accordingly, a user may initially identify types of data (e.g., by providing or identifying data samples305) that the user wishes to build a data segmentation1220around. For example, a user may ask DDN system140(which may include data managers210A-D and DDN manager230) to create a logical perimeter around personal financial information (PFI). For the sake of the following discussion, assume that data1210A includes PFI. Accordingly, a user may initially identify PFI in data1210A at data store111A. DDN system140may analyze data1210A as discussed earlier to identify other locations in system100where the same type of data is stored. DDN system140may learn of data1210A at data store111B.

Data managers210A and210B of DDN system140may monitor network traffic that enters and leaves data stores111A and111B, respectively, in order to collect information about the content and behavioral features345of data objects335having the relevant data for which the data segmentation1220is being built. As an example, data managers210A and210B may each identify, for their data store111, the applications that are requesting data objects that have PFI. The information collected by data managers210(which may include behavioral features345), in various embodiments, is sent to DDN manager230for further analysis. As discussed earlier, DDN manager230may generate DDN data structures225and train models227based on the information collected by data managers210.

In some embodiments, when generating DDN data structures225and training models227, DDN manager230may analyze differences in the information collected by different data managers210. As an example, the information collected by data manager210A may identify a particular automated teller machine (ATM) application that accesses data1210A in data store111A while the information collected by data manager210B may identify a particular online banking application that accesses data1210A in data store111B. Accordingly, DDN manager230may determine that the baseline behavior exhibited by data1210A should include being accessed by both the ATM and online banking applications. That is, DDN manager230may consolidate the information that is collected by different data managers210to generate DDN data structures225and to train models227that incorporate the various, different aspects found in that information.

After generating DDN data structures225and training models227, data manager230may push portions or all of that information to the appropriate data managers210. This may include storing such information in data stores220. Continuing the example from above, data manager230may send DDN data structures and models227to data managers210A and210B to allow for those data managers to protect data1210A. In some embodiments, a set of protection policies may be derived by DDN manager230based on DDN data structures225and models227. Such protection policies might, for example, include:Bank-customer-PFI-access-group has (online-banking-app, atm-app)Bank-customer-PFI allows access from bank-customer-pii-access-group
These protection policies may indicate that for the places where PFI data is located (e.g., data stores111A and111B), only two applications (i.e., the ATM and online banking applications) are allowed to access that PFI data, access attempts by other applications will be prevented. In various embodiments, data manager210sends the protection policies (which may be a part of a DDN data structure225and may include user-defined policies350) and models227to data managers210. In some cases, such information may be sent to only data managers210that are monitoring network traffic of data stores111that include the relevant data around which a data segmentation1220is built. Those data managers may then enforce those policies on any traffic that travels through them. Thus, a data segmentation1220may be built around data. That is, by dropping network traffic that deviates from the baseline behavior observed for a particular type of data, a perimeter may effectively be built around that type of data. Furthermore, by distributing DDN data structures225and models227associated with a particular type of data to the enforcement points throughout system100that are relevant to that type of data, that type of data may become segmented from other components including other data, even when that type of data is distributed throughout system100.

In various embodiments, multiple data segmentations1220may be built for the same system100. As shown inFIG.12for example, system100includes a data segmentation1220A (which contains data1210A) and a data segmentation1220B (which contains data1210B). In various cases, data segmentations1220may each be associated with DDN data structures225and models227that are different from other data segmentations1220. Accordingly, as shown by data manager210A inFIG.12, a data manager210may store different DDN data structures225(and/or models227). In various cases, when a particular data segmentation1220(e.g., data segmentation1220A) is compromised, other data segmentations1220(e.g., data segmentation1220B) may remain intact. For example, if a malicious actor gains access to data1210A stored at data store111A, the malicious actor may not gain access to data1210B that is stored at data store111A since it may be segmented separately from data1210A.

In some embodiments, DDN system140calculates an impact of distributing a certain DDN data structure225(or a portion of which that corresponds to the protection policies noted above) and/or model227to data managers210. For example, PFI may be co-located with some other type of data (e.g., personal medical information) on the same data store111. Accordingly, in some cases, a policy that limits access to the PFI to a list of systems may inadvertently limit access to the personal medical information. That is, the data store111may communicate with only systems on the list; all other data accesses by systems not on the list may be rejected and as such, a system that is not on the list that attempts to access the personal medical information, but not the PFI may still be rejected. This type of impact may be presented to a user so that the user may, for example, adjust the policy.

Implementing data-based segmentation may be advantageous over prior segmentation approaches as data-based segmentation may allow for easier scalability. For example, because data usage is relatively static when compared to workload usage inside of a modern data center, a user may need only a relatively small amount of data managers, which might not need to be moved between host systems very often. Moreover, segmenting systems into various networks while ensuring that those systems have access to the appropriate communication channels (as done in network-based segmentation) can be difficult and time consuming. In contrast, data-based segmentation does not have the issues of moving systems around to different networks, especially when new systems are constantly being added or removed.

Turning now toFIG.13A, a block diagram of various components of a setup phase1300is shown. Such components may be implemented via hardware or a combination of hardware and software routines. In the illustrated embodiment, setup phase1300includes data samples1310, a data processing engine1320, and a model generating engine1330. As further shown, data processing engine1320includes an algorithm1325. In some embodiments, setup phase1300may be implemented differently than shown, an example of which is discussed with respect toFIG.13B.

One particular situation in which data is more prone to security-based issues is when data is shared between different organizations since data leakage, whether it is intentional or unintentional, happens often. Such data leakage can happen because the data provider does not have any effective tools to ensure that the data consumer is handling the data correctly (e.g., in accordance with data usage policies that are set out by the data provider). For example, a data provider cannot control what portions of the data are shared by the data consumer with other organizations. If the data consumer were to instead provide their data processing algorithm to the data provider for processing data at the data provider's system (so that the data does not have to leave the data provider's system), then the data consumer has to be concerned about potentially losing intellectual property as their data processing algorithm is exposed to the data provider. Accordingly, with limited trust between the data provider and the data consumer and with limited visibility into the algorithm being used and how the data is being used, providing data to the data consumer or providing the data processing algorithm to the data provider are both risky propositions.

The present disclosure describes techniques for implementing an architecture in which data is shared among systems in a manner that overcomes some or all of the downsides of the prior approaches. In various embodiments described below, a data provider's system provides encrypted data to a data consumer's system that processes a decrypted form of the data within a verification environment running at the data consumer's system. If an output generated based on the decrypted form complies with data usage policies defined by the data provider, then that output may be permitted to be sent outside the verification environment. In some embodiments, if the verification environment detects abnormal behavior at the data consumer's system, then the verification environment prevents subsequent processing of the data provider's data by the data consumer's system. In various embodiments, sharing data from the data provider's system to the data consumer's system occurs in two phases: a setup phase and a sharing phase.

In the setup phase, a data provider may present (e.g., by uploading at a sharing service's system) dataset information to a data consumer that describes datasets that the data provider is willing to share. If the data consumer expresses interest in some dataset, then the data provider and the data consumer may define sharing information that identifies the dataset to be shared, the algorithm(s) to be executed for processing data of that dataset, and the data usage policies that control how that data may be handled. In various embodiments, as part of the setup phase, the data provider's system may provide data samples from the dataset to the data consumer's system. The data consumer's system may then execute the algorithms (identified by the sharing information) to process the data samples in order to produce an output that corresponds to the data samples. While the algorithms are executed, input and output (I/O) operations that occur at the data consumer's system may be tracked. The output and the tracked I/O operations may then be provided back to the data provider's system for review by the data provider to ensure that they comply with the data provider's data usage policies. If the output and I/O operations are compliant, then, in various embodiments, the output, the I/O operations, the data samples, and/or the data usage policies are used by the sharing service's system to train a verification model. In some embodiments, the execution flow of the data consumer's algorithms (e.g., the order in which the algorithm's methods are called) may be tracked and used to further train the verification model. After being trained, the verification model may be used to ensure that future output from the algorithms is compliant and the data consumer's system does not exhibit any abnormal behavior (e.g., I/O operations that are not allowed by the data provider's data usage policies) with respect to the behavior that may be observed during the setup phase. In various embodiments, the data samples, the output, and the verification model are associated with the sharing information.

In the sharing phase, the data provider's system may initially encrypt blocks of data for the dataset and then may send the encrypted data to the data consumer's system for processing by the data consumer's algorithms. The data consumer's system may, in various embodiments, execute a set of software routines (which may be provided by the sharing service's system, in some cases) to instantiate a verification environment in which the data consumer's algorithms may be executed to process the data provider's data. While the data provider's data is outside of the verification environment but at the data consumer's system, it may remain encrypted; however, within the verification environment, that data may be in a decrypted form that can be processed by the data consumer's algorithms.

In some embodiments, the verification environment serves as an intermediator between the data consumer's algorithms and entities outside the verification environment. Accordingly, to process data, the algorithms executing in the verification environment may request from the verification environment blocks of the data provider's data. The verification environment may retrieve a set of blocks of the data from a local storage and a set of corresponding decryption keys from the data provider's system. The verification environment may then decrypt the set of data blocks and provide them to the algorithms for processing, which may result in an output from the algorithms. In various embodiments, before an output from the algorithms can be sent outside the verification environment, it may be verified by the verification environment using the verification model that was trained during the setup phase. If the output complies with the data usage policies (as indicated by the verification model), then the output may be written to a location outside the verification environment; otherwise, the verification environment may notify the data provider's system that abnormal behavior (e.g., the generation of the prohibited output, the performance of prohibited network and/or disk I/O operations, etc.) occurred at the data consumer's system. In some instances, if the algorithms attempt to write data to a location that is outside the verification environment by circumventing the verification environment, then the verification environment may report this abnormal behavior to the data provider's system. In response to being notified that abnormal behavior has occurred at the data consumer's system, the data provider's system may stop sending decryption keys to the data consumer's system for decrypting blocks of the data provider's data. Accordingly, the data consumer may be unable to continue processing the data.

The data sharing architecture presented in the present disclosure may be advantageous as it allows for a data consumer to process data using their own algorithms while also affording a data provider with the ability to control how that data is handled outside of the data provider's system. That is, the verification environment and the verification model may provide assurance to the data provider that their data is secured by preventing information that is non-compliant from leaving the verification environment. The data consumer may have assurance that their algorithms will not be stolen as such algorithms may not be available outside of the verification environment. Thus, the data provider may share data with the data consumer in a manner that allows the data provider to protect their data and the data consumer to protect their algorithms.

Setup phase1300, in various embodiments, is a phase during which a data provider and a data consumer establish a framework for sharing data from the data provider's system to the data consumer's system. Accordingly, sharing information may be generated that enables that framework. In various embodiments, such information may identify a dataset to be shared, an algorithm1325that can be used to process data in that dataset, and/or a set of data usage policies for controlling management of that data, such policies may be defined by the data provider in some cases. A verification model1335may also be included in the sharing information and may be used to verify that the output generated by algorithm1325complies with the set of data usage policies. In various embodiments, verification model1335is trained based on data samples1310, outputs1327from data processing engine1320, and the behavioral features that are observed at the data consumer's system.

Data samples1310, in various embodiments, are samples of data from a dataset that may be shared from a data provider's system to a data consumer's system. The datasets that may be shared may include various fields that store user activity information (e.g., purchases made by a user), personal information (e.g., first and last names), enterprise information (e.g., business workflows), system information (e.g., network resources), financial information (e.g., account balances), and/or other types of information. Consider an example in which a dataset identifies user personal information such as first and last names, addresses, personal preferences, Social Security numbers, etc. In such an example, a data sample1310may specify a particular first and last name, a particular address, etc. In various embodiments, data samples1310may be publicly available so that an entity may, for example, download those data samples and configure their algorithm1325to process those data samples. Because the data provider may not have control over the usage of data samples1310, such data samples may include randomly generated values (or values that the data provider is not worried about being misused). In various embodiments, data samples1310are fed into data processing engine1320. This may be done to test algorithm1325and to assist in generating verification model1335.

Data processing engine1320, in various embodiments, generates outputs based on data shared by a data provider's system. As shown, data processing engine1320includes algorithm1325, which may process the shared data, including data samples1310. Algorithm1325, in various embodiments, is a set of software routines (which may be written by a data consumer) that are executable to extract or derive certain information from shared data. For example, an algorithm1325may be executed to process user profile data in order to output the preferences of users for certain products. As another example, an algorithm1325may be used to calculate the financial status of a user based on their financial history, which may be provided by the data provider's system to the data consumer's system. While only one algorithm1325is depicted inFIG.13A, in various embodiments, multiple algorithms1325may be used.

As part of setup phase1300, data samples1310may be provided to data processing engine1320to produce an output1327. Output1327, in various embodiments, is information that a data consumer (or, in some cases, a data provider) wishes to obtain from the data shared by the data provider's system. Output1327may, in various cases, be the result of executing algorithm1325on data samples1310. As explained above, the output from algorithm1325may be verified using a verification model1335; however, in setup phase1300, output from algorithm1325may be used to train a verification model1335. Accordingly, before verification model1335is trained based on output1327, output1327may be reviewed by the data provider (or another entity) to ensure that such an output is compliant with the data usage policies set out by the data provider. For example, a data provider may not want certain information such as Social Security numbers to be identifiable from output1327. Accordingly, output1327may thus be reviewed to ensure that it does not include such information. In some cases, after an output1327is identified to represent a valid/permissible output, it may be fed into model generating engine1330.

As explained further below, behavioral features (e.g., input and output operations) may be collected from the system executing data processing engine1320. For example, the locations to which algorithm1325writes data may be recorded. In various embodiments, the behavioral features may be sent with output1327to be reviewed by the data provider (or a third party). In some cases, other behavioral features such as the execution flow of algorithm1325may not be sent to the data provider, but instead provided (without being reviewed) to the system executing model generating engine1330for training verification model1335.

Model generating engine1330, in various embodiments, generates or trains verification models1335that may be used to verify the output from algorithm1325. In various embodiments, verification model1335is trained using artificial intelligence algorithms such as deep learning and/or machine learning-based algorithms that may receive output1327and the corresponding data samples1310as inputs. Accordingly, verification model1335may be trained based on the association between output1327and the corresponding data samples1310, such that verification model1335may be used to determine whether subsequent output matches the output expected for the data used to derive the subsequent output. Verification model1335may also be trained, based on the collected behavioral features, to detect abnormal behavior that might occur at the data consumer's system. After training, verification model1335may be included in the sharing information and used to verify subsequent output of algorithm1325that is based on data from the dataset being shared.

Turning now toFIG.13B, a block diagram of various components of a setup phase1300is shown. In the illustrated embodiment, setup phase1300includes a data provider system1340, a sharing service system1350, and a data consumer system1360. Also as shown, data provider system1340includes data samples1310, sharing service system1350includes model generating engine1330and sharing information1355, and data consumer system1360includes data processing engine1320. In some embodiments, setup phase1300may be implemented differently than shown. For example, sharing service system1350may not be included in setup phase1300; instead, data provider system1340may include model generating engine1330.

In various embodiments such as the one illustrated inFIG.13B, setup phase1300involves three parties: a data provider, a data consumer, and a sharing service. The data provider refers to an entity that shares data and the data consumer refers to an entity that processes the data in some manner. The sharing service may facilitate the sharing environment between the data provider and the data consumer by at least providing mechanisms for securing the exchange of data and the managing of that data while it is outside of data provider system1340. In some embodiments, sharing service system1350provides a secured gateway software appliance (not shown) that may be downloaded and installed at data provider system1340and data consumer system1360(after registering as an organization or user at sharing service system1350, in some cases). The secured gateway software appliance, in various embodiments, enables a computer system (e.g., data provider system1340or data consumer system1360) to securely communicate with a set of other computer systems. Accordingly, the secured gateway software appliance that is installed at systems1340and1360may enable those systems to securely communicate information between themselves. In some cases, the set of computer systems may not identify systems (e.g., sharing service system1350) other than systems1340and1360. Accordingly, the secured gateway software appliance that is installed at data provider system1340may not communicate with any other system than data consumer system1360(and vice versa). This may, in some instances, prevent the data consumer from leaking confidential information (e.g., the decryption keys) provided by the data provider. The secured gateway software appliance may further maintain sharing information1355and may instantiate a verification environment (discussed later) in which to execute data processing engine1320.

After the secured gateway software appliance has been installed (or, in some cases, as an independent event), data provider system1340may identify the various types of data that are stored by data provider system1340. In order to identify the various types of data, data provider system1340may discover locations where data is maintained and build data-defined network (DDN) data structures based on the data at those locations. As an example, data provider system1340may receive samples of data (e.g., personal information and financial information) from a data provider that the data provider wishes to share with a data consumer. Data provider system1340may then use those data samples to identify locations where the same or similar data is stored. The data samples and newly located data may be used to build a DDN data structure that identifies the data and its locations. In some embodiments, data provider system1340creates a catalog based on the DDN data structures that it built. The catalog may identify the datasets that data provider system1340may share with a data consumer. Accordingly, data provider system1340may share the catalog with a data consumer to allow that data consumer to choose which datasets that the data consumer wants to receive. In some embodiments, data provider system1340publishes or uploads the catalog to sharing service system1350, which may share the catalog with a data consumer. In some instances, data provider system1340may also publish data samples1310for the published datasets, although data provider system1340may, in other cases, provide data samples1310directly to data consumer system1360via the installed secured gateway software appliance. For example, data provider system1340may upload the catalog that indicates that the data provider is willing to share users' financial information and may include data samples1310of specific financial information.

When a data consumer expresses interest in a particular dataset, the data consumer and the data provider may negotiate on the details of how the data from the particular dataset may be used. This may include identifying what algorithm1325will be used to process the data and the data usage policies that facilitate control over how the data (which may include the outputs from algorithm1325) may be used. Examples of data usage policies include, but are not limited to, policies defining the time period in which the data may be accessed, who (e.g., what users) can execute algorithm1325, disk/network I/O permissions, output format, and privacy data that is to be filtered out of outputs from algorithm1325. In some embodiments, data usage policies are expressed in a computer programming language. In various embodiments, the parties that are involved in the data sharing process, the dataset being shared, the particular algorithm1325being used, and/or the data usage policies are defined in sharing information1355.

As part of setup phase1300, data consumer system1360may retrieve data samples1310from sharing service system1350(or data provider system1340in some embodiments) for the dataset being shared. Algorithm1325may be customized to fit the data samples1310(i.e., made to be able to process them) and then tested using those data samples to ensure that it can process the types of data included in the dataset being shared. In order to test algorithm1325, in various embodiments, data consumer system1360provides data processing engine1320to the installed secured gateway software appliance. Accordingly, the secured gateway software application may instantiate, at data consumer system1360, a verification environment (discussed in greater detail below) in which to execute data processing engine1320. Data samples1310may then be processed by algorithm1325to produce an output1327that may be provided to sharing service system1350. In various cases, algorithm1325may be tested using the approaches (discussed inFIGS.14A-D) that are actually used in the data sharing phase (expect without a verification model1335in various cases).

When testing algorithm1325, the secured gateway software appliance (installed at data consumer system1360) may monitor the behavior of data consumer system1360by monitoring various activities that occur at data consumer system1360. In various cases, the secured gateway software appliance may learn the execution flow of algorithm1325. In some embodiments, for example, algorithm1325may be run under a cluster-computing framework such as APACHE SPARK—data processing engine1320may implement APACHE SPARK. APACHE SPARK may generate directed acyclic graphs that describe the flow of execution of algorithm1325. For example, the vertices of a directed acyclic graph may represent the resilient distributed datasets (i.e., data structures of APACHE SPARK, which are an immutable collection of objects) and the edges may represent operations (e.g., the methods defined in the program code associated with algorithms1325) to be applied on the resilient distributed datasets. Accordingly, traversing through a directed acyclic graph generated by APACHE SPARK may represent a flow through the execution of algorithm1325. In various cases, when testing algorithm1325, multiple directed acyclic graphs may be generated that may include static portions that do not change between executions and dynamic portions that do change. The secured gateway software appliance may also observe disk and network I/O operations that occur when testing algorithm1325.

Subsequent to testing algorithm1325based on data samples1310, data consumer system1360may generate a digital signature of algorithm1325and output1327from algorithm1325(e.g., by hashing them). In various embodiments, data consumer system1360sends output1327, the behavioral information (e.g., the directed acyclic graphs and I/O operations), and the two digital signatures to service provide system1350to supplement sharing information1355and to assist in training verification model1335. In some cases, output1327and/or the information about the I/O operations may be first routed to data provider system1340for review by the data provider to ensure that they comply with the data usage policies (which may define the acceptable I/O operations) set out by the data provider. Once output1327and the I/O operations have been reviewed and approved, then they may be provided to sharing service system1350.

Based on data samples1310, output1327, the behavioral information, and the data usage policies specified by sharing information1355, model generating engine1330of sharing service system1350may generate a verification model1335. As mentioned earlier, verification model1335may, in part, be a modeling of the input and output data (e.g., data samples1310and output1327) that ensures that future output of algorithm1325complies with the data usage policies. In various embodiments, verification model1335includes a behavioral-based verification aspect and/or a data-defined verification aspect. The behavioral-based verification aspect may involve ensuring that the verification environment (discussed in more detail below) in which algorithm1325is executed has not been compromised (e.g., the kernel has not been modified), ensuring that the execution flow of algorithm1325is not irregular relative to the execution flow learned during setup phase1300, and ensuring that no I/O operations occur that are invalid with respect to the data usage policies defined in sharing information1355. The data-defined verification aspect may involve the removal of sensitive data fields in the data and enforcement of the output file format and output size limitations. After verification model1335has been generated, it may be included in sharing information1355.

Thereafter, in various embodiments, sharing information1355may be signed (e.g., using one or more cryptographic techniques) by data provider system1340and data consumer system1360. Sharing information1355may be maintained in the secured gateway software application at systems1340and1360, respectively. An example data sharing phase will now be discussed.

Turning now toFIG.14A, a block diagram of various components of a data sharing phase1400is shown. In the illustrated embodiment, data sharing phase1400includes data1410and a verification environment1420. As further depicted, verification environment1420includes data processing engine1320(having algorithm1325) and verification model1335. Data sharing phase1400, in some embodiments, may be implemented differently than shown. As illustrated inFIG.14Dfor example, verification environment1420may be split across multiple systems.

Data sharing phase1400, in various embodiments, is a phase in which the data provider shares data1410with a data consumer for processing by the data consumer's system. The data provider may progressively provide portions of data1410to the data consumer's system or may initially provide all of data1410to the data consumer's system before that data is subsequently processed. As an example, the data provider's system may enable the data consumer's system to process a first portion of data1410and then may verify the output from that processing (or receive an indication that the output has been verified) before enabling the data consumer's system to process a second portion of data1410. In either case, the data provider may prevent the data consumer's system from continuing the processing of data1410if the data provider's system determines that the data consumer has deviated from the data usage policies specified in sharing information1355.

Verification environment1420, in various embodiments, is a software wrapper routine that “wraps around” data processing engine1320and monitors data processing engine1320for deviations from the data usage policies (referred to as “abnormal behavior”) that are specified in sharing information1355. Since verification environment1420wraps around data processing engine1320, input/output that is directed to/from data processing engine1320may pass through verification environment1420. Accordingly, when data processing engine1320attempts to write an output1327to another location, verification environment1420may verify that output1327to ensure compliance before allowing it to be written to the location (e.g., a storage device of the data consumer's system).

In some embodiments, verification environment1420may be a sandbox environment in which data processing engine1320is executed. Accordingly, verification environment1420may restrict what actions that data processing engine1320can perform while also controlling input and output into and out of the sandbox. In various cases, during setup phase1300, verification environment1420may be modified/updated to support the architecture that is expected by data processing engine1320—that is, to be able to create the environment in which data processing engine1320can even execute.

As illustrated inFIG.14A, data1410passes through verification environment1420to data processing engine1320. In various embodiments, data1410may be provided to data processing engine1320by invoking an application programming interface of verification environment1420that causes verification environment1420to provide data1410to data processing engine1320. In some cases, the interface may be invoked by data processing engine1320itself when it wishes to process a portion or all of data1410; in other cases, the interface may be invoked by another system such as the data provider's system. In some embodiments, while data1410is outside of the data provider's system, data1410may be in an encrypted format to protect it. Accordingly, when sending data1410to data processing engine1320for processing, verification environment1420may first decrypt the encrypted version of data1410in order to provide a decrypted version to data processing engine1320. In order to decrypt data1410, verification environment1420may obtain decryption keys1415that are usable to decrypt portions of data1410—such decryption keys1415may be provided by the data provider's system. Accordingly, this may allow the data provider to control the data consumer's access to data1410as the data provider may continually provide keys1415to the data consumer's system for decrypting portions of data1410only while the data consumer's system is compliant with the data usage policies. If, for example, the data consumer's system exhibits abnormal behavior (e.g., the execution flow of algorithm1325has changed in a significant manner, an invalid I/O operation has been performed, etc.) with respect to some portion of data1410, then the data provider's system may not provide a decryption key1415for decrypting a subsequent portion of data1410.

Once data processing engine1320receives a decrypted portion of data1410, the portion may be fed into algorithm1325to produce an output1327. As mentioned above, when algorithm1325(or data processing engine1320) attempts to write output1327to a location outside of data processing engine1320, verification environment1420may verify that output to ensure that that output is compliant with the data usage policies specified in sharing information1355. In some embodiments, verification environment1420verifies an output1327by determining whether that output falls within a certain class or matches an expected output1327indicated by verification model1335based on the portion of data1410that was fed into algorithm1325. If an output1327is compliant, verification environment1420may write it (depicted as verified output1422) to the location requested by data processing engine1320; otherwise, that output may be discarded.

In various embodiments, verification environment1420may also monitor the activity of the data consumer's system for abnormal behavior. For example, verification environment1420may monitor I/O activity to determine if data processing engine1320is attempting to write an output1327outside of verification environment1420without that output being verified. In cases where abnormal behavior is detected, verification environment1420may report the behavior to the data provider's system (or another system). Accordingly, verification environment1420may send out a verification report1424. Verification report1424, in various embodiments, identifies whether an invalid output1327and/or abnormal behavior has been detected. In various cases, the data provider's system may decide to prevent data processing engine1320from processing additional portions of data1410based on verification report1424.

Turning now toFIG.14B, a block diagram of various components of a data sharing phase1400is shown. In the illustrated embodiment, data sharing phase1400includes a data provider system1340and a data consumer system1360. As illustrated, data provider system1340includes data1410, and data consumer system1360includes a verification environment1420having a data processing engine1320and a verification model1335.FIG.14Billustrates an example layout of the various components discussed with respect toFIG.14A. As shown, data provider system1340provides data1410and decryption keys1415to data consumer system1360, and data consumer system1360provides verification report1424(and, in various cases, verified output1422) to data provider system1340.

Turning now toFIG.14C, a block diagram of various components of a data sharing phase1400is shown.FIG.14Cillustrates another example layout of the various components discussed within the present disclosure. In the illustrated embodiment, data sharing phase1400includes a data provider system1340and a data consumer system1360. As illustrated, each of systems1340and1360includes a respective data store111and a respective secured gateway1450. Also as depicted, data consumer system1360includes a compute cluster1430that includes a verification environment1420having a data processing engine1320and a verification model1335. In some embodiments, data sharing phase1400may be implemented differently than shown, an example of which is discussed with respect toFIG.14D.

When beginning data sharing phase1400, in various embodiments, data provider system1340initially submits data blocks1445of data1410to secured gateway1450A (which, as discussed earlier, may be software routines downloaded from a sharing service system). One data block1445may correspond to a specific number of bytes of physical storage on a storage device such as a hard disk drive. For example, each data block1445may be 2 kilobytes in size. A file may, in some cases, comprise multiple data blocks1445. Accordingly, when sharing a given file with data consumer system1360for processing, data provider system1340may submit multiple data blocks1445to secured gateway1450A. Secured gateway1450A, in various embodiments, encrypts data blocks1445and then stores them at data store111A. Secured gateway1450A may create, for each data block1445, a decryption key1415that is usable to decrypt the corresponding data block1445, such keys1415may be sent to data consumer system1360during a later stage of data sharing phase1400.

After the relevant data blocks1445have been encrypted, data provider system1340may send those data blocks to data consumer system1360, which may then store them at data store111B for subsequent retrieval. As mentioned earlier, data provider system1340may build DDN data structures that identify the locations of where particular types of data (e.g., user financial information) are stored within data provider system1340. DDN data structures may, in various embodiments, store information about the history of how data is used. Accordingly, when data blocks1445are accessed by secured gateway1450A and sent to data consumer system1360, these events may be recorded in the relevant DDN data structure and may be reviewed by a user. In various cases, while data is being shared with data consumer system1360, a DDN data structure may include policies that allow for that data to be shared. But if the data provider or a user of that data decides to not provide that data to data consumer system1360, then the policies in the DDN data structure may be removed. Accordingly, in some embodiments, if there is an attempt to send that data to data consumer system1360, enforcers that implement the DDN data structure will prevent that data from being sent to data consumer system1360(as sending that data may be considered abnormal behavior, which is explained above).

Once data consumer system1360has begun to receive data blocks1445, data consumer system1360may submit a request to secured gateway1450B for initiating execution of algorithm1325. Accordingly, in various embodiments, secured gateway1450B submits a request (depicted as “Start Algorithm Execution”) to compute cluster1430to instantiate verification environment1420, which (as discussed earlier) may serve as a sandbox (or other type of virtual environment) in which data processing engine1320(and thus algorithm1325) is executed.

As explained above, verification environment1420may provide a file access application programming interface (API) that enables algorithm1325to access data blocks1445by invoking the API. In response to receiving a request from algorithm1325for accessing a set of data blocks1445, verification environment1420may retrieve encrypted data blocks1445from data store111B and may issue a key request to secured gateway1450B for the respective decryption keys1415that are usable for decrypting those data blocks. In various cases, verification environment1420may be limited on the number of decryption keys1415that it may retrieve, at a given point, from secured gateway1450B. This limit may be imposed by data provider system1340to control data consumer system1360's access to data blocks1445. As an example, in some embodiments, secured gateway1450A may provide only one decryption key1415to secured gateway1450B before secured gateway1450B has to provide back a verification report1424in order to receive another decryption key1415. By sending a limited number of decryption keys1415at a time to data consumer system1360, data provider system1340may control data consumer system1360's access to data blocks1445so that if a problem occurs (e.g., data consumer system1360violates a data usage policy defined in sharing information1355), then data provider system1340may protect the rest of data blocks1445(which may be stored in data store111B) by not allowing them to be decrypted. That is, data provider system1340may not initially grant data consumer system1360access to all the relevant encrypted data blocks1445, but instead may incrementally provide access (e.g., by incrementally supplying decryption keys1415) while the data consumer is compliant the data usage policies set out in sharing information1355. Once a decryption key1415has been received from secured gateway1450B, verification environment1420may decrypt the respective data block1445and provide that data block to algorithm1325. Algorithm1325may then process that decrypted data block (as if it were directly loaded from a data storage).

After processing one or more data blocks1445, algorithm1325may attempt to write the output to a location outside of verification environment1420. Accordingly, algorithm1325may invoke an API of verification environment1420to write the output to the location. At that point, in various embodiments, verification environment1420verifies whether the output is compliant based on verification model1335. For example, verification environment1420may determine if the output corresponds to an expected output derived by inputting the one or more data blocks1445into verification model1335. If compliant, then verified output1422may be stored in a data storage (e.g., data store111B) of data consumer system1360. In some embodiments, output from algorithm1325may be encrypted (e.g., by secured gateway1450B) and provided to data provider system1340for examination. Upon passing the examination, verified output1422may be provided back to data consumer system1360and stored in a decrypted format. Subsequently, algorithm1325may request additional data blocks1445, which may be provided if data consumer system1360has not exhibited abnormal behavior. That is, data provider system1340may not provide additional decryption keys1415to enable additional data blocks1445to be processed if abnormal behavior is detected.

During data sharing phase1400, verification environment1420may monitor the behavior of data consumer system1360. If abnormal behavior (which may include invalid output, disk or network I/O operations that are not allowed by the data usage policies, etc.) is detected, such abnormal behavior may be reported to data provider system1340in verification report1424. For example, based on verification model1335, verification environment1420(or, in some instances, secured gateway1450B) may determine that the execution flow of algorithm1325has deviated enough from the execution flow observed during setup phase1300—that is, the directed acyclic graphs generated for algorithm1325during the data sharing phase1400deviate in a significant enough manner from those generated for algorithm1325during the setup phase1300. This type of irregularity may be reported in a verification report1424that is sent to data provider system1340. Verification report1424may, in some cases, be sent to data provider system1340in response to verifying an output from algorithm1325. If data provider system1340determines, based on a verification report1424, that abnormal behavior has occurred at data consumer system1360, then data provider system1340may stop providing decryption keys1415to data consumer system1360—stopping data consumer system1360from processing subsequent data blocks1445. Otherwise, if no abnormal behavior has been detected, then data provider system1340may send subsequent decryption keys1415to data consumer system1360to enable subsequent data blocks1445to be processed. In some embodiments, verification environment1420may terminate data processing engine1320if abnormal behavior is detected and/or reject requests for subsequent data blocks1445.

In some embodiments, the information provided in a verification report1424is recorded in the DDN data structure that corresponds to the data that was sent to data consumer system1360. This information may become a part of the history of how that data is used. Accordingly, a user may be able to track the progress of how the data is currently being used by reviewing the history information in the DDN data structure.

Data sharing phase1400, in some embodiments, may involve data consumer system1360processing data1410, but not having access to verified output1422. That is, the data provider, in some instances, may wish to use the data consumer's algorithm1325without exposing data1410to the data consumer. Accordingly, verified output1422may be encrypted (e.g., using keys1415that were used to decrypt data blocks1445for processing) and sent back to data provider system1360.

Turning now toFIG.14D, a block diagram of various components of a data sharing phase1400is shown.FIG.14Dillustrates another example layout of the various components discussed within the present disclosure. In the illustrated embodiment, data sharing phase1400includes a data provider system1340, a sharing service system1350, and a data consumer system1360. As illustrated, data provider system1340includes data1410; sharing service system1350includes a verification environment1420A having a verification model1335; and data consumer system1360includes a verification environment1420B having data processing engine1320.

In some embodiments, instead of output1327from algorithm1325being verified at data consumer system1360, output1327may be sent to sharing service system1350for verification by verification environment1420A. As an example, in some cases, when data processing engine1320attempts to write output1327from algorithm1325to a location that is outside of verification environment1420B, then verification environment1420B may send an encrypted version of that output to verification environment1420A. Verification environment1420A may then determine whether output1327is compliant using verification model1335. If that output is compliant, then verification environment1420A may send verified output1422to data consumer system1360and may send verification report1424to data provider system1340so that system1340may provide subsequent decryption keys1415to data consumer system1360. If the output is not compliant, then verification environment1420A may send verification report1424to data provider system1340so that system1340may not provide subsequent decryption keys1415and output1327may be discarded.

Turning now toFIG.15, a flow diagram of a method1500is shown. Method1500is one embodiment of a method performed by a first computer system such as data consumer system1360to process data shared by a second computer system such as data provider system1340. In some embodiments, method1500may include additional steps. For example, the first computer system may receive, from a third computer system (e.g., sharing service system1350), a set of program instructions (e.g., program instructions that implement secured gateway1450) that are executable to instantiate a verification environment (e.g., verification environment1420) in which to process shared data.

Method1500begins in step1510with the first computer system receiving data (e.g., data1410, which may be received as data blocks1445) shared by a second computer system to permit the first computer system to perform processing of the data according to a set of policies (e.g., policies of sharing information1355) specified by the second computer system. The shared data may be received in an encrypted format.

In step1520, the first computer system instantiates a verification environment in which to process the shared data.

In step1530, the first computer system processes a portion of the shared data (e.g., a set of data blocks1445) by executing a set of processing routines (e.g., algorithm1325) to generate a result (e.g., output1327) based on the shared data. In some embodiments, processing a portion of the shared data includes requesting, from the verification environment by one of the set of processing routines, a set of data blocks included in the shared data and accessing, by the verification environment, a set of decryption keys (e.g., decryption keys1415) from the second computer system for decrypting the set of data blocks. Processing a portion of the shared data may also include generating, by the verification environment using the set of decryption keys, decrypted versions of the set of data blocks and processing, by ones of the set of processing routines, the decrypted versions within the verification environment.

In step1540, the verification environment of the first computer system verifies whether the result is in accordance with the set of policies specified by the second computer system. In various embodiments, the verification environment of the first computer system may determine whether the set of processing routines have exhibited abnormal behavior according to the set of policies specified by the second computer system. Such abnormal behavior may include a given one of the set of processing routines performing an input/output-based operation that is not permitted by the set of policies specified by the second computer system. In some instances, in response to determining that the set of processing routines exhibited abnormal behavior, the verification environment may terminate the set of processing routines. In some instances, in response to determining that the set of processing routines have exhibited abnormal behavior, the verification environment may reject subsequent requests by the set of processing routines for data blocks included in the shared data.

In step1550, the verification environment of the first computer system determines whether to output (e.g., as verified output1422) the result based on the verifying.

In step1560, the verification environment of the first computer system sends an indication (e.g., verification report1424) of an outcome of the determining to the second computer system. The indication may be usable by the second computer system to determine whether to provide the first computer system with continued access to the shared data (e.g., to determine whether to provide subsequent decryption keys1415).

In various embodiments, the first computer system receives an initial set of data (e.g., data samples1310) that is shared by the second computer system. The first computer system may process the initial set of data by executing the set of processing routines to generate a particular result that is based on the initial set of data. The particular result may be usable to derive a verification model (e.g., verification model1335) for verifying whether a given result generated based on the data shared by the second computer system is in accordance with the set of policies specified by the second computer system. The first computer system may provide the particular result to a third computer system, which may be is configured to derive a particular verification model based on the set of initial data, the particular result, and the set of policies. The first computer system may then receive, from the third computer system, the particular verification model. Accordingly, verifying whether the result is in accordance with the set of policies may include determining whether the result corresponds to an acceptable result that is indicated by the particular verification model based on the portion of the shared data.

Turning now toFIG.16, a flow diagram of a method1600is shown. Method1600is one embodiment of a method performed by a first computer system such as data consumer system1360to process data shared by a second computer system such as data provider system1340. In some embodiments, method1600may be performed by executing a set of program instructions stored on a non-transitory computer-readable medium. In some embodiments, method300may include additional steps. For example, the data shared by the second computer system may be in an encrypted format and thus first computer system may receive, from the second computer system, a set of decryption keys (e.g., decryption keys1415) usable to decrypt a portion of the shared data.

Method1600begins in step1610with the first computer system receiving data (e.g., data1410) shared by a second computer system to permit the first computer system to perform processing of the data according to one or more policies identified by the second computer system.

In step1620, the first computer system processes a portion of the shared data. In various embodiments, processing the portion includes instantiating a verification environment (e.g., verification environment1420) in which to process the portion of the shared data and causing execution of a set of processing routines (e.g., algorithm1325) in the verification environment to generate a result (e.g., output1327) based on the shared data. Processing the portion may also include verifying whether the result is in accordance with the one or more policies and determining whether to enable the result to be written outside the verification environment based on the verifying. In some embodiments, verifying whether the result is in accordance with the one or more policies may include verifying the result based on one or more machine learning-based models (e.g., verification model1335) trained based on the one or more policies and previous output (e.g., output1327based on data samples1310) from the set of processing routines.

In step1630, the first computer system sends an indication (e.g., verification report1424) of an outcome of the determining to the second computer system. The indication may be usable by the second computer system to determine whether to provide the first computer system with continued access to the shared data. In some cases, the indication may indicate a determination to enable the result to be written outside the verification environment.

In some embodiments, the first computer system monitors the first computer system for behavior that deviates from behavior indicated by the one or more policies. In response to detecting that the first computer system has exhibited behavior that deviates from behavior indicated by the one or more policies, the first computer system may prevent the set of processing routines from processing subsequent portions of the shared data.

Turning now toFIG.17, a flow diagram of a method500is shown. Method500is one embodiment of a method performed by a sharing service computer system (e.g., sharing service system1350) to provide a verification model (e.g., verification model1335) usable to verify output from a data consumer computer system (e.g., data consumer system1360). In some embodiments, method500may include additional steps. For example, the sharing service computer system may send sharing information (e.g., sharing information1355) to the data consumer computer system and a data provider computer system (e.g., data provider system1340).

Method500begins in step1710with the sharing service computer system receiving, from the data provider computer system, information that defines a set of policies that affect processing of data (e.g., data1410) that is shared by the data provider computer system with the data consumer computer system.

In step1720, the sharing service computer system receives, from the data consumer computer system, a set of results (e.g., output1327) derived by processing a particular set of data (e.g., data samples1310) shared by the data provider computer system with the data consumer computer system. In some embodiments, the particular set of data is shared by the data provider computer system with the data consumer computer system via the sharing service computer system. Accordingly, the sharing service computer system may receive, from the data provider computer system, the particular set of data and may send, to the data consumer computer system, the particular set of data for deriving the set of results.

In step1730, based on the particular set of data, the set of results, and the set of policies, the sharing service computer system generates a verification model for verifying whether a given result generated by the data consumer computer system based on a given portion of data shared by the data provider computer system is in accordance with the set of policies.

In step1740, the sharing service computer system sends, to the data consumer computer system, the verification model for verifying whether results generated based on data shared by the data provider computer system is in accordance with the set of policies. The sharing service computer system, in some embodiments, sends, to the data consumer computer system, a set of program instructions that are executable to instantiate a verification environment in which to process data shared by the data provider computer system with the data consumer computer system. The verification environment may be operable to prevent results generated based on data shared by the data provider computer system that are not in accordance with the set of policies from being sent outside of the verification environment. The verification environment may also be operable to monitor the data consumer computer system for abnormal behavior and to provide an indication (e.g., verification report1424) to the data provider computer system of abnormal behavior detected at the data consumer computer system.

Turning now toFIG.18, a block diagram of a method flow1800is shown. In the illustrated embodiment, method flow1800includes data discovery and segmentation stage1810, data usage and approval stage1820, and data sharing stage1830. In some embodiments, method flow1800may be implemented differently than depicted. For example, method flow1800may include a behavior learning stage that may occur at a data provider system.

Method flow1800, in various embodiments, is a series of stages implemented to enable a data provider system to identify data managed by the data provider system and to enable the use of the data (e.g., by sharing with a data consumer system) in accordance with authorizations obtained from users of that data. As explained further below, method flow1800may enable the data provider to comply with data ownership and privacy regulations (such as the European Union's General Data Protection Regulation 2016/679 (GDPR)) by facilitating an environment in which users may control the usage of their data.

In various embodiments, method flow1800starts with data discovery and segmentation stage1810. As explained in greater detail with respect toFIGS.1-12, a user's data may often be scattered around different layers of a network with poor structuring and visibility. The user data may take many forms, including personally identifiable information (PII) of the user, medical or financial data of the user, data stored by a social media website such as FACEBOOK, TWITTER, LINKEDIN, or INSTAGRAM, etc. Personal user information may be stored in structured formats (e.g., stored in database tables) or unstructured formats (e.g., stored in PDFs, WORD documents, etc.) across different storage devices. Thus, in many cases, data providers lack an understanding of the different types of data (e.g., medical data, financial data, etc.) that they have and where that data is stored. Various techniques are discussed with respect toFIGS.1-12for discovering what data is stored at a data provider system and for segmenting that data into different data segmentations that may be used to protect the data within those data segmentations. Some of the techniques will be briefly discussed here, but a fuller description is provided with respect toFIGS.1-12.

As explained with respect toFIGS.1-12, similarity detection and machine learning techniques may be used to identify data objects (e.g., files such as PDFs, WORD documents, etc.) having similar data content (e.g., similar data fields). In various embodiments, a user associated with a data provider system provides or identifies samples of data objects that serve as a basis for discovering similar data managed by the data provider system. For example, a user may be presented with an interface through which the user may specify samples of user personal data (e.g., by identifying locations of data objects that store user personal data). Various techniques may be used to identify other data objects having the same or similar content. Such techniques may include using a piecewise hashing technique to compare hash values between data objects (where similar hash values may indicate that data objects have similar content) and/or training content classification models based on content features of data objects in order for the models to be able to identify other data objects with the same or similar content features.

As part of the data discovery process, the network traffic of a data provider system may be evaluated to extract data objects and to identify whether the data objects are similar to other data objects (e.g., by using the techniques mentioned above) that have been classified. In some embodiments, when similar data objects are discovered, the locations where those data objects originated from may be evaluated to determine if there are other similar data objects. As such, locations that were previously unknown by the data provider to store relevant data objects may be discovered and revealed to the data provider. In some embodiments, the data managers that monitor network traffic may include network scanners that may be used to scan the data stores throughout the data provider system for relevant data objects.

During data discovery and segmentation stage1810, data-defined network (DDN) data structures may be generated that incorporate multiple dimensions of relevant data attributes to logically group data objects that have similar content. As noted with respect toFIGS.1-12, a DDN data structure indicates a set of data that matches some similarity criteria (in this particular context, that set of data might be all data of a particular user on a data provider's computer systems, although a DDN data structure may be created for each type of data that is associated with that particular user), along with indicating content features such as data fields and values, as well as a set of behaviors (uses) that are permitted for that data. For example, a DDN data structure may identify a protection policy indicating that the associated data objects can be accessed by only certain applications or systems. The use of the DDN paradigm may facilitate enforcement of the protection policy by pushing the DDN data structure (or portions of it such as the policy) to data managers that enforce the policy on data objects extracted from network traffic belonging to the DDN data structure associated with the policy. For example, if a data object is being sent to a prohibited application, then it may be dropped from the network traffic by a data manager. By dropping data objects from network traffic that deviate from the protection policies, a data segmentation may effectively be built around data associated with a user. Such a data segmentation may protect data objects from, for example, malicious use, unintentional misuse, or uses that deviate from management policies such as those defined by corporations or governmental entities.

In some applications described with respect toFIGS.1-12, the data discovery stage1810may seek to understand typical uses of data within a computer network in order to populate the behavioral portion of a DDN data structure. But in method1800, the behavioral portions of a various DDN data structure may be defined with a desired set of behaviors or protection. Thus, after a user's personal data is discovered, a DDN data structure for that user may be populated with a set of behaviors that are desired for that user's data. For example, a DDN data structure for that user may be defined to comply with governmental regulations such as GDPR. As will be described below, a user may be presented with a number of different proposed data usage proposals, each of which may be set up to correspond to a different DDN data structure that may be defined for that user. The user may accept or reject various ones of these data usage proposals and as a result, protection policies may be created that restrict or allow for the flow of particular data objects associated with the user to data consumer systems and/or within the data provider system. Data discovery, DDN data structures, policies, and data segmentation are discussed in greater detail with respect toFIGS.1-12.

In the data usage and approval stage1820, users may be presented with the data that the data provider system stores for that user. For example, a user may be presented with their own personal data, which may include, but is not limited to, contact data, geolocation data, various application usage data, browsing data, call data, etc. The user may, in some cases, select the various data items to acquire further transparency into the discovered and collected data. In various embodiments, the one or more DDN data structures created for a particular user may be used in determining what data is stored for that user and can presented to the user upon request.

In some instances, particular data may be erroneously associated with a particular user when discovered or that data may be inconsistent. For example, two files may be discovered where one of them indicates that the particular user is a male while the other indicates that the user is a female. Accordingly, in various embodiments, a user or data subject may be presented with a user interface that allows for the user to confirm the correctness or accuracy of data that is identified as belonging to that user. That user may correct any incorrect data associations or data inconsistencies. Continuing with the above example, the user may indicate that she is a female. In some cases, a user may not be able to view or accept data usage proposals (discussed below) until the user has reviewed the particular instances flagged by the data provider system as potentially being incorrect data associations or inconsistent. Corrections that the user makes to these data associations or inconsistencies may be used to further train the models used in the discovery stage1810, to correct the appropriate DDN data structures, and/or to correct the data objects themselves.

In various embodiments, user may be presented with data usage proposals (or products) that define an arrangement in which particular data of the user may be used by the data provider or a data consumer to achieve a particular end. Such data usage proposals may define the types of data that will be used, how that data will be used, who will use that data, how long that data may be used for, who will be compensated for that data, what the compensation will be, how that data will be secured and shared with a data consumer (if applicable), and other information that may be used to assess whether to approve a data usage proposal. These data products can be considered analogous to financial products or instruments, with the definition of the data product being considered equivalent to the prospectus for a financial product. In some cases, the definition of a data product that specifies how a user's data will be used might constitute a binding legal document.

For example, a data usage proposal may be presented to a user indicating that the user's geolocation data will be shared with a telecommunication service provider and that the user will be compensated with a particular amount of money (e.g., $5 per month, for a period of one year). The compensation may be in different forms, which may include a financial credit, a service credit (e.g., a new feature associated with the telecommunications company), and/or a product credit (e.g., a new telecommunications device or accessory). A user may accept or reject certain data usage proposals that are presented to the user. As such, a user may have control over how their data is used. Once a user makes one or more choices during stage1820, DDN data structures may be updated to define permissible data usages. This paradigm allows a user such as a telecommunications provider with millions of users to effectively segment user data, for example by creating a DDN data structure for each individual user, and allowing different usages for each individual user by tailoring the behavioral specifications of each of these numerous DDN data structures (based on particular users selecting different data products that are presented to them).

Stage1820may be accomplished through any suitable user interface. For example, the user interface may be presented to the user as part of their already-existing account login with the data provider. Alternately, a third-party website might be used to present the user with the various available data products. Note that the user assent for proposed data usages may be given in various forms, including paper signatures, biometric authorization, text message authorization, etc.

During data sharing stage1830, data objects that were authorized by a user may thus be used according to the approved usages. Such usage may either be internal or external to the data provider. As explained with respect toFIGS.13-17, data sharing may occur in two phases: a setup phase and a sharing phase. In some embodiments, during the setup phase, the data provider system provides samples of data to one or more data consumer systems. Such samples may correspond to the various types of data that were discovered during stage1810. The data consumer system may process the samples (e.g., by executing algorithms as discussed with respect toFIGS.13-17) to produce outputs. The samples, the outputs, and a set of policies (which were agreed to by the data provider and the data consumer) may be used to train a verification model that can be used to verify subsequent outputs by the data consumer system for compliance with the set of policies. In the context of method1800, such policies will have previously presented to a user as part of a data usage proposal.

During the sharing phase, the data provider system may provide the data (indicated by a data usage proposal) to a data consumer system in an encrypted format. The data consumer system, in some embodiments, instantiates a verification environment in which to process the provided data using the data consumer's algorithms. While the data is within the verification environment, it may be in a decrypted format that can be processed to produce an output. The output may be verified by the verification environment using the verification model trained in the setup phase. The verification environment may also check the execution flow of the data consumer's algorithm to ensure that the execution flow is similar to what was observed in the setup phase. If the verification environment detects a prohibited output or abnormal behavior (e.g., a change to the execution flow of the consumer's algorithm, invalid input and/or output operations, etc.), the verification environment may notify the data provider system. In response to such a notification, the data provider system may stop providing decryption keys that are usable for decrypting the data, stopping the data consumer system from continuing processing of the provided data. In this manner, the data provider system may ensure that the user's data is protected in accordance with the data usage proposal that the user accepted. This process is described in detail with respect toFIGS.13-17.

It is noted that in many instances, it is desirable for secure data sharing to have the following four characteristics: 1) data usage can be monitored and audited in real time across infrastructure both internally and externally; 2) data usage cannot be use for a non-specified purpose; 3) data cannot be copied and redistributed except as specified; and 4) data usage history will be automatically recorded in a manner that is tamper-proof (e.g., by writing to a blockchain ledger).

Turning now toFIG.19, a block diagram of an example architecture for implementing method1800is shown. In the illustrated embodiments, the architecture includes a data provider system1340and two data consumer systems1360A and1360B. As further shown, data provider system1340includes data stores111A,111B, and111C that store user data1210A and a data store111D that stores user data1210B. Data provider system1340also includes data managers210A-D that manage access to data stores111A-D, respectively. As shown, data segmentations1220A and1220B encompass user data1210A and1210B, respectively. As shown, data consumer system1360A may include a verification environment1420A having a data processing engine1320A while data consume system1360B may include a verification environment1420B having a data processing engine1320A. In some embodiments, the example architecture may be implemented differently than shown—e.g., the architecture may include a sharing service system that may provide at least a verification model.

As explained earlier, a data provider system may be evaluated to determine what user data is stored by that system. Data provider system1340may discover user data1210A stored at data stores111A,111B, and111C. Accordingly, a DDN data structure may be generated that identifies user data1210A at those data stores111. In a similar way, a DDN data structure may be generated that identifies user data1210B at data store111D. Users associated with the DDN data structures may be presented with data usage proposals in which data consumer systems1360A and1360B wish to process user data1210A and1210B.

User interface engine1940, in various embodiments, generates user interfaces that may be presented to users. Such user interfaces may include elements that display information about a user's data (e.g., user data1210A) within data provider system1340and about proposed usages of that data. For example, one user interface may display what types of data that data provider system1340stores for a particular user. That user interface may allow a user to create policies around the user's data, including what systems may access and use that data. In various cases, user interface engine1940may generate user interfaces that present data usage proposals to users that may decide to accept or reject those proposals.

A user of user data1210A may allow for data consumer system1360A to process user data1210A, but not allow for data consumer system1360B to process that data. Accordingly, protection policies may be generated and sent to data managers210A-C, which may permit user data1210to be sent to data consumer system1360A, but data consumer system1360B as shown in the illustrated embodiment. A user of user data1210B, however, may allow for both data consumer systems1360A and1360B to process data1210B.

Turning now toFIG.20, a block diagram of an example user interface2000that displays data usage proposals is shown. In the illustrated embodiment, user interface2000includes data usage proposals2010A and2010B. As depicted, data usage proposal2010A indicates that a data consumer (company A) wants to access particular data (location data) of the user and that the user will be financially compensated with $100. As further depicted, data usage proposal2010B indicates that another data consumer (company B) wants to access another particular type of data (history of cellular data usage) of the user and that the user will be compensated with 4 GB of cellular data. The user may select, for each data usage proposal2010, whether the user agrees or rejects the data usage proposal. The user may also select a detail tab2020on each data usage proposal2010in order to see details about the proposal. Such details may include those listed in the above description of data usage proposals—e.g., how the data will be used, how long the data will be used for, etc. In some cases, if a user rejects data usage proposal, a protection policy may be created prevents a particular data consumer from accessing the requested data. That protection policy may be included in the relevant DDN data structure and then distributed data managers throughout the data provider system to enforce the protection policy. In some cases, if the user accepts a data usage proposal, then a protection policy may be created that allows for particular data to be sent to a particular data consumer.

The paradigm of method1800has several benefits. Discovery stage1810allows entities to fully discover and encapsulate each individual user's data independent of location and infrastructure within their distributed networks. Approval stage1820allows users and data providers to clarify data ownership and permissible usages by creating legally binding contracts as specified by detailed data product prospectuses that are agreed to by the consumer. This creates greater transparency for the users as to the precise nature of the data stored by the data provider, as well as how (if at all) that data is legally permitted to be used. The usages agreed to in stage1820allow for the creation of various DDN data structures that are set up to have permissible behaviors that correspond to those usages agreed to in stage1820. By precisely defining data and its permitted uses, this allows data providers and consumers to then use this data with confidence in stage1830, as usage will be in accordance with data security considerations, any internal data usage policies, and any applicable governmental or third-party regulations.

This approach thus has the potential to stimulate a true data economy. Big Data currently exists, but such data is unfortunately used only for the benefit of a few select data providers, without any recompense for the individual users themselves. Current regulatory approaches have shifted this paradigm, and the methods described herein address this problem by providing an incentive to establish data identification, ownership, and agreed-upon data usage policies. Data can thus be sold or rented to third parties, with the proceeds going to the data owner, and possibly a portion going to the data provider. In the current approach, social media sites and the like have accumulated massive amounts of user data—right now, such data is being marketed to third parties without any financial benefit to the users themselves. Unfortunately, users will ultimately bear the costs of such sharing if there is ultimately a misuse or breach of this data. This paradigm helps ensure that when data is shared, there is a sound technical approach in place to make sure that the sharing is in accordance with agreed-upon data usage specifications. This method will thus help foster sharing of data when it is permitted, while ensuring that such sharing is in compliance with corporate or governmental regulations.

Turning now toFIG.21, a flow diagram of a method2100is shown. Method2100is one embodiment of a method performed by a computer system (e.g., a data provider computer system1340) to facilitate the sharing of user data according to a usage policy. Method2100may be performed by executing a set of program instructions stored on a non-transitory computer-readable medium. In some embodiments, method2100may include additional steps. For example, the computer system may present a user interface (e.g., a user interface520) to a user for configuring different aspects (e.g., user-defined policies350) of the computer system.

Method2100begins in step2110with the computer system storing particular data (e.g., user data1210) of a user. The computer system may identify the particular data as corresponding to the user, including by evaluating one or more databases (e.g., data stores111) to group data objects (e.g., data objects335) based on content of those data objects satisfying a set of similarity criteria. A particular group of data objects may correspond to the particular data of the user. In some embodiments, the computer system generates a set of data-defined network (DDN) data structures (e.g., DDN data structures225) that logically groups the data objects of the particular group independent of physical infrastructure via which those data objects are stored, communicated, or utilized.

In step2120, the computer system commences sharing of a portion of the particular data with a data consumer computer system (e.g., data consumer system1360). The set of data-defined network (DDN) data structures may construct a data segmentation (e.g., a data segmentation1220) having the data objects of the particular group. The data segmentation may be associated with a set of usage policies (e.g., user-defined policies350) defining permissible types of access to data objects within the data segmentation. In response to receiving permission to perform the sharing, the computer system may add a particular usage policy to the set of usage policies that permits portions of the particular data to be sent to the data consumer computer system. In response to being denied permission to continue sharing portions of the particular data, the computer system may add a particular usage policy to the set of usage policies that prevents portions of the particular data from being sent to the data consumer computer system.

In some embodiments, the computer system presents a user interface (e.g., a user interface2000) indicating a proposed usage of the particular data by the data consumer computer system according to the specified usage policy. The computer system may receive, from the user via the user interface, permission for the data consumer computer system to utilize the particular data according to the specified usage policy and the commencing sharing may be performed based on receiving the permission.

In step2130, the computer system continues sharing of additional portions of the particular data with the data consumer computer system in response to receiving a report (e.g., a verification report1424) from a verification environment (e.g., a verification environment1420) indicating that the particular data is being utilized by the data consumer computer system in accordance with a specified usage policy. The computer system may send, to the data consumer computer system, the additional portions of the particular data in an encrypted format. In some cases, continuing sharing of additional portions of the particular data may include: in response to receiving the report that indicates that the particular data is being utilized in accordance with the specified usage policy, the computer system sending, to the data consumer computer system, a set of decryption keys (e.g., decryption keys1415) usable for decrypting the encrypted additional portions of the particular data. The computer system may receive a second report from the verification environment indicating that the particular data is not being utilized by the data consumer computer system in accordance with the specified usage policy. As such, the computer system may discontinue sharing of subsequent additional portions of the particular data with the data consumer computer system. The specified usage policy specifies a length of time that the particular data may be used by the data consumer computer system.

Turning now toFIG.22, a flow diagram of a method2200is shown. Method2200is one embodiment of a method performed by a computer system (e.g., a data provider computer system1340) to facilitate the sharing of user data according to a usage policy. Method2200begins in step2210with the computer system providing data samples (e.g., data samples1310) to a model provider service (e.g., a sharing service system1350) to build a verification model (e.g., a verification model1335) for verifying that a data usage policy is being followed on a data consumer computer system (e.g., a data consumer system1360). In step2220, the computer system presents, to a user, a proposed usage (e.g., a data usage proposal2010) of particular data (e.g., user data1210) of the user by the data consumer computer system according to the data usage policy. In step2230, the computer system receives, from the user, input indicating that the proposed usage is acceptable. In step2240, in response to the input, the computer system causes an initial portion of the particular data (e.g., encrypted data blocks1445) to be shared with the data consumer computer system. In step2250, the computer system receives a report (e.g., verification report1424) indicating that the data consumer computer system is using the particular data in accordance with the data usage policy. The report may be generated based on the verification model. In step2260, in response to receiving the indication, causing additional portions of the particular data to be shared with the data consumer computer system.

Exemplary Computer System

Turning now toFIG.23, a block diagram of an exemplary computer system2300, which may implement system100, data provider system1340, sharing service system1350, and/or data consumer system1360, is depicted. Computer system2300includes a processor subsystem2380that is coupled to a system memory2320and I/O interfaces(s)2340via an interconnect2360(e.g., a system bus). I/O interface(s)2340is coupled to one or more I/O devices2350. Computer system2300may be any of various types of devices, including, but not limited to, a server system, personal computer system, desktop computer, laptop or notebook computer, mainframe computer system, tablet computer, handheld computer, workstation, network computer, a consumer device such as a mobile phone, music player, or personal data assistant (PDA). Although a single computer system2300is shown inFIG.23for convenience, system2300may also be implemented as two or more computer systems operating together.

Processor subsystem2380may include one or more processors or processing units. In various embodiments of computer system2300, multiple instances of processor subsystem2380may be coupled to interconnect2360. In various embodiments, processor subsystem2380(or each processor unit within2380) may contain a cache or other form of on-board memory.

System memory2320is usable store program instructions executable by processor subsystem2380to cause system2300perform various operations described herein. System memory2320may be implemented using different physical memory media, such as hard disk storage, floppy disk storage, removable disk storage, flash memory, random access memory (RAM-SRAM, EDO RAM, SDRAM, DDR SDRAM, RAMBUS RAM, etc.), read only memory (PROM, EEPROM, etc.), and so on. Memory in computer system2300is not limited to primary storage such as memory2320. Rather, computer system2300may also include other forms of storage such as cache memory in processor subsystem2380and secondary storage on I/O Devices2350(e.g., a hard drive, storage array, etc.). In some embodiments, these other forms of storage may also store program instructions executable by processor subsystem2380. In some embodiments, program instructions that when executed implement data store111, data manager210, user interface engine1940, verification environment1420, and data processing engine1320may be included/stored within system memory2320.

I/O interfaces2340may be any of various types of interfaces configured to couple to and communicate with other devices, according to various embodiments. In one embodiment, I/O interface2340is a bridge chip (e.g., Southbridge) from a front-side to one or more back-side buses. I/O interfaces2340may be coupled to one or more I/O devices2350via one or more corresponding buses or other interfaces. Examples of I/O devices2350include storage devices (hard drive, optical drive, removable flash drive, storage array, SAN, or their associated controller), network interface devices (e.g., to a local or wide-area network), or other devices (e.g., graphics, user interface devices, etc.). In one embodiment, computer system2300is coupled to a network via a network interface device2350(e.g., configured to communicate over WiFi, Bluetooth, Ethernet, etc.).