Data clean room

Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

TECHNICAL FIELD

The present disclosure generally relates to securely analyzing data across different accounts using a data clean room.

BACKGROUND

Currently, most digital advertising is performed using third-party cookies. Cookies are small pieces of data generated and sent from a web server and stored on the user's computer by the user's web browser that are used to gather data about customers' habits based on their website browsing history. Because of privacy concerns, the use of cookies is being restricted.

Companies may want to create target groups for advertising or marketing efforts for specific audience segments. To do so, companies may want to compare their customer information with that of other companies to see if their customer lists overlap for the creation of such target groups. Thus, companies may want to perform data analysis, such as an overlap analysis, of their customers or other data. To perform such types of data analyses, companies can use “trusted” third parties, who can access data from each of the companies and perform the data analysis. However, this third-party approach suffers from significant disadvantages. First, companies give up control of their customer data to these third parties, which can lead to unforeseen and harmful consequences because this data can contain sensitive information, such as personal identity information. Second, the analysis is performed by the third parties, not the companies themselves. Thus, the companies have to go back to the third parties to conduct a more detailed analysis or a different analysis. This can increase the expense associated with the analysis as well as add a time delay. Also, providing such information to third parties for this purpose may run afoul of ever-evolving data privacy regulations and common industry policies.

DETAILED DESCRIPTION

Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. For example, the output may be restricted based on a minimum threshold of overlapping data (e.g., elements per output data row). Therefore, each account (e.g., company, party) may keep control of its data in its own account while being able to perform data analysis using its own data and data from other accounts. Each account may set policies for which types of data and which types of analysis it is willing to allow other accounts to perform. The overlap data may be anonymized to prevent sensitive information from being revealed.

FIG. 1illustrates an example shared data processing platform100implementing secure messaging between deployments, in accordance with some embodiments of the present disclosure. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted from the figures. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the shared data processing platform100to facilitate additional functionality that is not specifically described herein.

As shown, the shared data processing platform100comprises the network-based data warehouse system102, a cloud computing storage platform104(e.g., a storage platform, an AWS® service, Microsoft Azure®, or Google Cloud Services®), and a remote computing device106. The network-based data warehouse system102is a network-based system used for storing and accessing data (e.g., internally storing data, accessing external remotely located data) in an integrated manner, and reporting and analysis of the integrated data from the one or more disparate sources (e.g., the cloud computing storage platform104). The cloud computing storage platform104comprises a plurality of computing machines and provides on-demand computer system resources such as data storage and computing power to the network-based data warehouse system102. While in the embodiment illustrated inFIG. 1, a data warehouse is depicted, other embodiments may include other types of databases or other data processing systems.

The remote computing device106(e.g., a user device such as a laptop computer) comprises one or more computing machines (e.g., a user device such as a laptop computer) that execute a remote software component108(e.g., browser accessed cloud service) to provide additional functionality to users of the network-based data warehouse system102. The remote software component108comprises a set of machine-readable instructions (e.g., code) that, when executed by the remote computing device106, cause the remote computing device106to provide certain functionality. The remote software component108may operate on input data and generates result data based on processing, analyzing, or otherwise transforming the input data. As an example, the remote software component108can be a data provider or data consumer that enables database tracking procedures, such as streams on shared tables and views, as discussed in further detail below.

The network-based data warehouse system102comprises an access management system110, a compute service manager112, an execution platform114, and a database116. The access management system110enables administrative users to manage access to resources and services provided by the network-based data warehouse system102. Administrative users can create and manage users, roles, and groups, and use permissions to allow or deny access to resources and services. The access management system110can store share data that securely manages shared access to the storage resources of the cloud computing storage platform104amongst different users of the network-based data warehouse system102, as discussed in further detail below.

The compute service manager112coordinates and manages operations of the network-based data warehouse system102. The compute service manager112also performs query optimization and compilation as well as managing dusters of computing services that provide compute resources (e.g., virtual warehouses, virtual machines, EC2 dusters). The compute service manager112can support any number of client accounts such as end users providing data storage and retrieval requests, system administrators managing the systems and methods described herein, and other components/devices that interact with compute service manager112.

The compute service manager112is also coupled to database116, which is associated with the entirety of data stored on the shared data processing platform100. The database116stores data pertaining to various functions and aspects associated with the network-based data warehouse system102and its users.

In some embodiments, database116includes a summary of data stored in remote data storage systems as well as data available from one or more local caches. Additionally, database116may include information regarding how data is organized in the remote data storage systems and the local caches. Database116allows systems and services to determine whether a piece of data needs to be accessed without loading or accessing the actual data from a storage device. The compute service manager112is further coupled to an execution platform114, which provides multiple computing resources (e.g., virtual warehouses) that execute various data storage and data retrieval tasks, as discussed in greater detail below.

Execution platform114is coupled to multiple data storage devices124-1to124-nthat are part of a cloud computing storage platform104. In some embodiments, data storage devices124-1to124-nare cloud-based storage devices located in one or more geographic locations. For example, data storage devices124-1to124-nmay be part of a public cloud infrastructure or a private cloud infrastructure. Data storage devices124-1to124-nmay be hard disk drives (HDDs), solid state drives (SSDs), storage clusters, Amazon S3 storage systems or any other data storage technology. Additionally, cloud computing storage platform104may include distributed file systems (such as Hadoop Distributed File Systems (HDFS)), object storage systems, and the like.

The execution platform114comprises a plurality of compute nodes (e.g., virtual warehouses). A set of processes on a compute node executes a query plan compiled by the compute service manager112. The set of processes can include: a first process to execute the query plan; a second process to monitor and delete micro-partition files using a least recently used (LRU) policy, and implement an out of memory (OOM) error mitigation process; a third process that extracts health information from process logs and status information to send back to the compute service manager112; a fourth process to establish communication with the compute service manager112after a system boot; and a fifth process to handle all communication with a compute cluster for a given job provided by the compute service manager112and to communicate information back to the compute service manager112and other compute nodes of the execution platform114.

The cloud computing storage platform104also comprises an access management system118and a web proxy120. As with the access management system110, the access management system118allows users to create and manage users, roles, and groups, and use permissions to allow or deny access to cloud services and resources. The access management system110of the network-based data warehouse system102and the access management system118of the cloud computing storage platform104can communicate and share information so as to enable access and management of resources and services shared by users of both the network-based data warehouse system102and the cloud computing storage platform104. The web proxy120handles tasks involved in accepting and processing concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. The web proxy120provides HTTP proxy service for creating, publishing, maintaining, securing, and monitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shared data processing platform100are implemented via one or more data communication networks. These data communication networks may utilize any communication protocol and any type of communication medium. In some embodiments, the data communication networks are a combination of two or more data communication networks (or sub-networks) coupled to one another. In alternate embodiments, these communication links are implemented using any type of communication medium and any communication protocol.

As shown inFIG. 1, data storage devices124-1to124-N are decoupled from the computing resources associated with the execution platform114. That is, new virtual warehouses can be created and terminated in the execution platform114and additional data storage devices can be created and terminated on the cloud computing storage platform104in an independent manner. This architecture supports dynamic changes to the network-based data warehouse system102based on the changing data storage/retrieval needs as well as the changing needs of the users and systems accessing the shared data processing platform100. The support of dynamic changes allows network-based data warehouse system102to scale quickly in response to changing demands on the systems and components within network-based data warehouse system102. The decoupling of the computing resources from the data storage devices124-1to124-nsupports the storage of large amounts of data without requiring a corresponding large amount of computing resources. Similarly, this decoupling of resources supports a significant increase in the computing resources utilized at a particular time without requiring a corresponding increase in the available data storage resources. Additionally, the decoupling of resources enables different accounts to handle creating additional compute resources to process data shared by other users without affecting the other users' systems. For instance, a data provider may have three compute resources and share data with a data consumer, and the data consumer may generate new compute resources to execute queries against the shared data, where the new compute resources are managed by the data consumer and do not affect or interact with the compute resources of the data provider.

Compute service manager112, database116, execution platform114, cloud computing storage platform104, and remote computing device106are shown inFIG. 1as individual components. However, each of compute service manager112, database116, execution platform114, cloud computing storage platform104, and remote computing environment may be implemented as a distributed system (e.g., distributed across multiple systems/platforms at multiple geographic locations) connected by APIs and access information (e.g., tokens, login data). Additionally, each of compute service manager112, database116, execution platform114, and cloud computing storage platform104can be scaled up or down (independently of one another) depending on changes to the requests received and the changing needs of shared data processing platform100. Thus, in the described embodiments, the network-based data warehouse system102is dynamic and supports regular changes to meet the current data processing needs.

During typical operation, the network-based data warehouse system102processes multiple jobs (e.g., queries) determined by the compute service manager112. These jobs are scheduled and managed by the compute service manager112to determine when and how to execute the job. For example, the compute service manager112may divide the job into multiple discrete tasks and may determine what data is needed to execute each of the multiple discrete tasks. The compute service manager112may assign each of the multiple discrete tasks to one or more nodes of the execution platform114to process the task. The compute service manager112may determine what data is needed to process a task and further determine which nodes within the execution platform114are best suited to process the task. Some nodes may have already cached the data needed to process the task (due to the nodes having recently downloaded the data from the cloud computing storage platform104for a previous job) and, therefore, be a good candidate for processing the task. Metadata stored in the database116assists the compute service manager112in determining which nodes in the execution platform114have already cached at least a portion of the data needed to process the task. One or more nodes in the execution platform114process the task using data cached by the nodes and, if necessary, data retrieved from the cloud computing storage platform104. It is desirable to retrieve as much data as possible from caches within the execution platform114because the retrieval speed is typically much faster than retrieving data from the cloud computing storage platform104.

As shown inFIG. 1, the shared data processing platform100separates the execution platform114from the cloud computing storage platform104. In this arrangement, the processing resources and cache resources in the execution platform114operate independently of the data storage devices124-1to124-nin the cloud computing storage platform104. Thus, the computing resources and cache resources are not restricted to specific data storage devices124-1to124-n. Instead, all computing resources and all cache resources may retrieve data from, and store data to, any of the data storage resources in the cloud computing storage platform104.

FIG. 2is a block diagram illustrating components of the compute service manager112, in accordance with some embodiments of the present disclosure. As shown inFIG. 2, a request processing service202manages received data storage requests and data retrieval requests (e.g., jobs to be performed on database data). For example, the request processing service202may determine the data necessary to process a received query (e.g., a data storage request or data retrieval request). The data may be stored in a cache within the execution platform114or in a data storage device in cloud computing storage platform104. A management console service204supports access to various systems and processes by administrators and other system managers. Additionally, the management console service204may receive a request to execute a job and monitor the workload on the system. The stream share engine225manages change tracking on database objects, such as a data share (e.g., shared table) or shared view, according to some example embodiments, and as discussed in further detail below.

The compute service manager112also includes a job compiler206, a job optimizer208, and a job executor210. The job compiler206parses a job into multiple discrete tasks and generates the execution code for each of the multiple discrete tasks. The job optimizer208determines the best method to execute the multiple discrete tasks based on the data that needs to be processed. The job optimizer208also handles various data pruning operations and other data optimization techniques to improve the speed and efficiency of executing the job. The job executor210executes the execution code for jobs received from a queue or determined by the compute service manager112.

A job scheduler and coordinator212sends received jobs to the appropriate services or systems for compilation, optimization, and dispatch to the execution platform114. For example, jobs may be prioritized and processed in that prioritized order. In an embodiment, the job scheduler and coordinator212determines a priority for internal jobs that are scheduled by the compute service manager112with other “outside” jobs such as user queries that may be scheduled by other systems in the database but may utilize the same processing resources in the execution platform114. In some embodiments, the job scheduler and coordinator212identifies or assigns particular nodes in the execution platform114to process particular tasks. A virtual warehouse manager214manages the operation of multiple virtual warehouses implemented in the execution platform114. As discussed below, each virtual warehouse includes multiple execution nodes that each include a cache and a processor (e.g., a virtual machine, a operating system level container execution environment).

Additionally, the compute service manager112includes a configuration and metadata manager216, which manages the information related to the data stored in the remote data storage devices and in the local caches (i.e., the caches in execution platform114). The configuration and metadata manager216uses the metadata to determine which data micro-partitions need to be accessed to retrieve data for processing a particular task or job. A monitor and workload analyzer218oversees processes performed by the compute service manager112and manages the distribution of tasks (e.g., workload) across the virtual warehouses and execution nodes in the execution platform114. The monitor and workload analyzer218also redistributes tasks, as needed, based on changing workloads throughout the network-based data warehouse system102and may further redistribute tasks based on a user (e.g., “external”) query workload that may also be processed by the execution platform114. The configuration and metadata manager216and the monitor and workload analyzer218are coupled to a data storage device220. Data storage device220inFIG. 2represent any data storage device within the network-based data warehouse system102. For example, data storage device220may represent caches in execution platform114, storage devices in cloud computing storage platform104, or any other storage device.

FIG. 3is a block diagram illustrating components of the execution platform114, in accordance with some embodiments of the present disclosure. As shown inFIG. 3, execution platform114includes multiple virtual warehouses, which are elastic clusters of compute instances, such as virtual machines. In the example illustrated, the virtual warehouses include virtual warehouse1, virtual warehouse2, and virtual warehouse n. Each virtual warehouse (e.g., EC2 duster) includes multiple execution nodes (e.g., virtual machines) that each include a data cache and a processor. The virtual warehouses can execute multiple tasks in parallel by using the multiple execution nodes. As discussed herein, execution platform114can add new virtual warehouses and drop existing virtual warehouses in real time based on the current processing needs of the systems and users. This flexibility allows the execution platform114to quickly deploy large amounts of computing resources when needed without being forced to continue paying for those computing resources when they are no longer needed. All virtual warehouses can access data from any data storage device (e.g., any storage device in cloud computing storage platform104).

Each virtual warehouse is capable of accessing any of the data storage devices124-1to124-nshown inFIG. 1. Thus, the virtual warehouses are not necessarily assigned to a specific data storage device124-1to124-nand, instead, can access data from any of the data storage devices124-1to124-nwithin the cloud computing storage platform104. Similarly, each of the execution nodes shown inFIG. 3can access data from any of the data storage devices124-1to124-n. For instance, the storage device124-1of a first user (e.g., provider account user) may be shared with a worker node in a virtual warehouse of another user (e.g., consumer account user), such that the other user can create a database (e.g., read-only database) and use the data in storage device124-1directly without needing to copy the data (e.g., copy it to a new disk managed by the consumer account user). In some embodiments, a particular virtual warehouse or a particular execution node may be temporarily assigned to a specific data storage device, but the virtual warehouse or execution node may later access data from any other data storage device.

In the example ofFIG. 3, virtual warehouse1includes three execution nodes302-1,302-2, and302-n. Execution node302-1includes a cache304-1and a processor306-1. Execution node302-2includes a cache304-2and a processor306-2. Execution node302-nincludes a cache304-nand a processor306-n. Each execution node302-1,302-2, and302-nis associated with processing one or more data storage and/or data retrieval tasks. For example, a virtual warehouse may handle data storage and data retrieval tasks associated with an internal service, such as a clustering service, a materialized view refresh service, a file compaction service, a storage procedure service, or a file upgrade service. In other implementations, a particular virtual warehouse may handle data storage and data retrieval tasks associated with a particular data storage system or a particular category of data.

Similar to virtual warehouse1discussed above, virtual warehouse2includes three execution nodes312-1,312-2, and312-n. Execution node312-1includes a cache314-1and a processor316-1. Execution node312-2includes a cache314-2and a processor316-2. Execution node312-nincludes a cache314-nand a processor316-n. Additionally, virtual warehouse3includes three execution nodes322-1,322-2, and322-n. Execution node322-1includes a cache324-1and a processor326-1. Execution node322-2includes a cache324-2and a processor326-2. Execution node322-nincludes a cache324-nand a processor326-n.

Although the execution nodes shown inFIG. 3each include one data cache and one processor, alternative embodiments may include execution nodes containing any number of processors and any number of caches. Additionally, the caches may vary in size among the different execution nodes. The caches shown inFIG. 3store, in the local execution node (e.g., local disk), data that was retrieved from one or more data storage devices in cloud computing storage platform104(e.g., S3 objects recently accessed by the given node). In some example embodiments, the cache stores file headers and individual columns of files as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in the node caches, the job optimizer208assigns input file sets to the nodes using a consistent hashing scheme to hash over table file names of the data accessed (e.g., data in database116or database122). Subsequent or concurrent queries accessing the same table file will therefore be performed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically in response to environmental conditions (e.g., disaster scenarios), hardware/software issues (e.g., malfunctions), or administrative changes (e.g., changing from a large cluster to smaller cluster to lower costs). In some example embodiments, when the set of nodes changes, no data is reshuffled immediately. Instead, the least recently used replacement policy is implemented to eventually replace the lost cache contents over multiple jobs. Thus, the caches reduce or eliminate the bottleneck problems occurring in platforms that consistently retrieve data from remote storage systems. Instead of repeatedly accessing data from the remote storage devices, the systems and methods described herein access data from the caches in the execution nodes, which is significantly faster and avoids the bottleneck problem discussed above. In some embodiments, the caches are implemented using high-speed memory devices that provide fast access to the cached data. Each cache can store data from any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary between different execution nodes. For example, one execution node may contain significant computing resources and minimal cache resources, making the execution node useful for tasks that require significant computing resources. Another execution node may contain significant cache resources and minimal computing resources, making this execution node useful for tasks that require caching of large amounts of data. Yet another execution node may contain cache resources providing faster input-output operations, useful for tasks that require fast scanning of large amounts of data. In some embodiments, the execution platform114implements skew handling to distribute work amongst the cache resources and computing resources associated with a particular execution, where the distribution may be further based on the expected tasks to be performed by the execution nodes. For example, an execution node may be assigned more processing resources if the tasks performed by the execution node become more processor-intensive. Similarly, an execution node may be assigned more cache resources if the tasks performed by the execution node require a larger cache capacity. Further, some nodes may be executing much slower than others due to various issues (e.g., virtualization issues, network overhead). In some example embodiments, the imbalances are addressed at the scan level using a file stealing scheme. In particular, whenever a node process completes scanning its set of input files, it requests additional files from other nodes. If the one of the other nodes receives such a request, the node analyzes its own set (e.g., how many files are left in the input file set when the request is received), and then transfers ownership of one or more of the remaining files for the duration of the current job (e.g., query). The requesting node (e.g., the file stealing node) then receives the data (e.g., header data) and downloads the files from the cloud computing storage platform104(e.g., from data storage device124-1), and does not download the files from the transferring node. In this way, lagging nodes can transfer files via file stealing in a way that does not worsen the load on the lagging nodes.

Although virtual warehouses1,2, and n are associated with the same execution platform114, the virtual warehouses may be implemented using multiple computing systems at multiple geographic locations. For example, virtual warehouse1can be implemented by a computing system at a first geographic location, while virtual warehouses2and n are implemented by another computing system at a second geographic location. In some embodiments, these different computing systems are cloud-based computing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown inFIG. 3as having multiple execution nodes. The multiple execution nodes associated with each virtual warehouse may be implemented using multiple computing systems at multiple geographic locations. For example, an instance of virtual warehouse1implements execution nodes302-1and302-2on one computing platform at a geographic location and implements execution node302-nat a different computing platform at another geographic location. Selecting particular computing systems to implement an execution node may depend on various factors, such as the level of resources needed for a particular execution node (e.g., processing resource requirements and cache requirements), the resources available at particular computing systems, communication capabilities of networks within a geographic location or between geographic locations, and which computing systems are already implementing other execution nodes in the virtual warehouse.

Execution platform114is also fault tolerant. For example, if one virtual warehouse fails, that virtual warehouse is quickly replaced with a different virtual warehouse at a different geographic location.

FIG. 4shows an example of two separate accounts in a data warehouse system, according to some example embodiments. Here, Company A may operate an account A402with a network-based data warehouse system as described herein. In account A402, Company A data404may be stored. The Company A data404may include, for example, customer data406relating to customers of Company A. The customer data406may be stored in a table or other format storing customer information and other related information. The other related information may include identifying information, such as email, and other known characteristics of the customers, such as gender, geographic location, purchasing habits, and the like. For example, if Company A is a consumer-goods company, purchasing characteristics may be stored, such as whether the customer is single, married, part of a suburban or urban family, etc. If Company A is a streaming service company, information about the watching habits of customers may be stored, such as whether the customer likes sci-fi, nature, reality, action, etc.

Likewise, Company B may operate an account B412with the network-based data warehouse system as described herein. In account B412, Company B data414may be stored. The Company B data414may include, for example, customer data relating customers of Company B. The customer data416may be stored in a table or other format storing customer information and other related information. The other related information may include identifying information, such as email, and other known characteristics of the customers, such as gender, geographic location, purchasing habits, etc., as described above.

For security reasons, Company A's data may not be accessible to Company B and vice versa. However, Company A and Company B may want to share at least some of their data with each other without revealing sensitive information, such as a customer's personal identity information. For example, Company A and Company B may want to explore cross marketing or advertising opportunities and may want to see how many of their customers overlap and filter based on certain characteristics of the overlapping customers to identify relationships and patterns.

To this end, a data clean room may be provided by the network-based data warehouse system as described herein.FIG. 5is a block diagram illustrating a method for operating a data clean room, according to some example embodiments. The data clean room may enable companies A and B to perform overlap analysis on their company data, without sharing sensitive data and without losing control over the data. The data clean room may create linkages between the data for each account and may include a set of blind cross reference tables.

Next, example operations to create the data clean room are described. Account B may include customer data502for Company B, and account A may include customer data504for Company A. In this example, account B may initiate the creation of the data clean room; however, either account may initiate creation of the data clean room. Account B may create a secure function506. The secure function506may look up specific identifier information in account B's customer data502. The secure function506may anonymize the information by creating identifiers for each customer data (e.g., generating a first result set). The secure function506may be a secure user-defined function (UDF) and may be implemented using the techniques described in U.S. patent application Ser. No. 16/814,875, entitled “System and Method for Global Data Sharing,” filed on Mar. 10, 2020, which is incorporated herein by reference in its entirety, including but not limited to those portions that specifically appear hereinafter, the incorporation by reference being made with the following exception: In the event that any portion of the above-referenced application is inconsistent with this application, this application supersedes the above-referenced application.

The secure function506may be implemented as a SQL UDF. The secure function506may be defined to protect the underlying data used to process the function. As such, the secure function506may restrict direct and indirect exposure of the underlying data.

The secure function506may then be shared with account A using a secure share508. The secure share508may allow account A to execute the secure function506while restricting account A from having access to the underlying data of account B used by the function and from being able to see the code of the function. The secure share508may also restrict account A from accessing the code of the secure function506. Moreover, the secure share508may restrict account A from seeing any logs or other information about account B's use of the secure function506or the parameters provided by account B of the secure function506when it is called.

Account A may execute the secure function506using its customer data504(e.g., generating a second result set). The result of the execution of the secure function506may be communicated to account B. For instance, a cross reference table510may be created in account B, which may include anonymized customer information512(e.g., anonymized identification information). Likewise, a cross reference table514may be created in account A, which may include anonymized customer information516for matching overlapping customers for both companies, and dummy identifiers for non-matching records. The data from the two companies may be securely joined so that neither account may access the underlying data or other identifiable information. For example, the data may be securely joined using the techniques described in U.S. patent application Ser. No. 16/368,339, entitled “Secure Data Joins in a Multiple Tenant Database System,” filed on May 28, 2019, which is incorporated herein by reference in its entirety, including but not limited to those portions that specifically appear hereinafter, the incorporation by reference being made with the following exception: In the event that any portion of the above-referenced application is inconsistent with this application, this application supersedes the above-referenced application.

For instance, cross reference table510(and anonymized customer information512) may include fields: “my_cust_id,” which may correspond to the customer ID in account B's data; “my_link_id,” which may correspond to an anonymized link to the identified customer information; and a “their_link_id,” which may correspond to an anonymized matched customer in company A. “their_link_id” may be anonymized, so that company B cannot discern the identity of the matched customers. The anonymization may be performed using hashing, encryption, tokenization, or other suitable techniques.

Moreover, to further anonymize the identity, all listed customers of company B in cross reference table510(and anonymized customer information512) may have a unique matched customer from company B listed, irrespective of whether there was an actual match or not. A dummy “their_link_id” may be created for customers not matched. This way neither company may be able to ascertain identity information of the matched customers. Neither company may discern where there is an actual match rather than a dummy returned identifier (no match). Hence, the cross reference tables510may include anonymized key-value pairs. A summary report may be created notifying the total number of matches, but other details of the matched customers may not be provided to safeguard the identities of the customers.

The data clean room may operate in one or both directions, meaning that a double-blind clean room may be provided.FIG. 6illustrates is a block diagram illustrating a method for operating a double-blind clean room, according to some example embodiments. The double-blind clean room may enable company A to perform overlap analysis using its company data with the company data of Company B and vice versa, without sharing sensitive data and without losing control over their own data. The double-blind clean room may create linkages between the data for each account and may include a set of double-blind cross reference tables.

Here, account A may include its customer data602, and account B may include its customer data604. Account A may create a secure function606(“Get_Link_ID”), as described above. The secure function606may be shared with account B using a secure share608, as described above. Moreover, a stored-procedures function610may detect changes to data in respective customer data and may update and refresh links accordingly.

The same or similar process may be applied from account B to account A with secure function612, secure share614, and stored procedures616. Consequently, the cross reference table618in account A may include information about the customer overlap between the two companies. For example, the cross reference table618includes fields: “my_cust_id,” which may correspond to the customer ID in account A's data; “my_link_id,” which may correspond to an anonymized link to the identified customer information of company A; and a “their_link_id,” which may correspond to an anonymized matched customer in company B. The anonymization may be performed using hashing, encryption, tokenization, or the like.

Similarly, the cross reference table620in account B may include information about the customer overlap between the two companies. For example, the cross reference table620includes fields: “my_cust_id,” which may correspond to the customer ID in account B's data; “my_link_id,” which may correspond to an anonymized link to the identified customer information of company B; and a “their_link_id,” which may correspond to an anonymized matched customer in company A. The anonymization may be performed using hashing, encryption, tokenization, or other suitable techniques.

In the above examples both company A and B had accounts with the data warehouse system. However, the blind clean room techniques described herein may also find applications when one or both companies do not have accounts with the data warehouse system.FIG. 7illustrates a technique for on-boarding data to a data warehouse system, according to some example embodiments. Here, Company C may not have an account with the data warehouse system but may still nonetheless want to employ the data clean room techniques described herein. Company C may load its company data into a load file702(e.g., a .csv file). Then, using a browser704or an app or the like, Company C may use a file uploader706upload the load file702to a secure cloud storage location708(also referred to as “enclave bucket”). From the enclave bucket708, the data may be moved into an enclave account710. The data may be moved by a batch data ingestion command, a copy command, or the like. A custom web GUI712may also be used to send control information to the enclave account710. The control information, for example, may set access restrictions for the data in the load file702. For the purpose of creating and using a data clean room, the enclave account710may then function and operate as a regular account, as described above.

After a data clean room is created, the parties may run secure queries on the secure data to gather more detailed information. In the example of securely matching customers of two companies, either company may send a query request to the other company to determine the count of matches based on select conditions.

FIGS. 8A-8Cis a block diagram illustrating a method for processing a secure query using a data clean room, according to some example embodiments. The example inFIGS. 8A-8Cbuilds on the example data clean room created inFIG. 6. Here, account A may include its customer data602, and account B may include its customer data604. As explained above, cross reference tables618,620may be created and included in accounts A and B, respectively. In addition, sales data802(shown inFIG. 8C), which includes information related to customers' buying habits, may be included in account A, and watch data804, which may include information about customers' viewing habits, may be included in account B.

Moreover, account B may use a secure view806to allow company A to have access to select data, referred to as secure query usable data808. Account B may notify account A of this secure query usable data808in a variety of ways. Account B may publish this information to account A in advance. It may share the structure and lookup keys of the secure query usable data808with account A. It may also use a private data exchange using the techniques described in U.S. patent application Ser. No. 16/746,673, entitled “Private Data Exchange,” filed on Jan. 17, 2020, which is incorporated herein by reference in its entirety, including but not limited to those portions that specifically appear hereinafter, the incorporation by reference being made with the following exception: In the event that any portion of the above-referenced application is inconsistent with this application, this application supersedes the above-referenced application.

A user in account A may run a secure query810in the data clean room. The query may request processing of data in both accounts A and B, but may restrict the user from having access to sensitive data of account B. For example, the user may run a query requesting information: How many of my (Company A's) customers have watched one of Company B's programs, grouped by Company B programs and my (Company A's) segments also bought my “Top Paper Towels” product, who I know live in the US or Canada, and who are also animation fans according to Company B, where there are at least two customers in each resulting group?

Using Company A's customer data602, cross reference table618, and sales data802, the query may generate an interim table812(“MyData1”). The interim table812, in this example, may include anonymized Company A's customer information for customers who bought “Top Paper Towels” and who live in the US or Canada securely joined with matching anonymized Company B customers (which may or may not include “dummy” matched accounts as described above). A secure query request814may also be generated to send to Company B. The secure query request814may request Company B to run the remaining portion of the query and to send back the final results. For example, the secure query request814may include a request ID, filters for Company B to apply (“select_c” and “where_c”), and an output format for the final results (“result_table”). The secure query request814may be provided in the form of a request table (as shown) or may be another type of remote procedure call, such as a SQL statement. The interim table812and secure query request814may be shared with account B using secure query request share816.

Next, account B may complete the remaining portion of the original query. At account B, a copy of the interim table818may be stored. The secure query request814may be received by a stream on request820and task on new request822functions. The secure query may then be executed by a restricted query procedure function824. This function may use data from a variety of sources, such as the copy of the interim table818, the cross reference table620, the watch data804, and the secure query usable data808, to execute the query. In this example, the restricted query procedure function824may filter customers identified in the interim table818by those who have watched one of Company B's program and are animation fans, and the function may group the results by the program and Company A's segments, as shown in a query status826. The results may be generated and output to an interim result table828. Next, the last part of the query may be performed: filter out results with fewer than two customers in each resulting group (e.g., a minimum threshold). In the example shown inFIGS. 8A-8C, the group for “Movie B” and a particular Company A segment had only one result, so it was removed. The final results may then be shared with account A using a secure query result share function830. The results in account A may be provided as a final result table832(“res1”) showing the matches of the query grouped by the two programs.

FIG. 9illustrates a diagrammatic representation of a machine900in the form of a computer system within which a set of instructions may be executed for causing the machine900to perform any one or more of the methodologies discussed herein, according to an example embodiment. Specifically,FIG. 9shows a diagrammatic representation of the machine900in the example form of a computer system, within which instructions916(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine900to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions916may cause the machine900to execute any one or more operations of any one or more of the methods described herein. As another example, the instructions916may cause the machine900to implemented portions of the data flows described herein. In this way, the instructions916transform a general, non-programmed machine into a particular machine900(e.g., the remote computing device106, the access management system110, the compute service manager112, the execution platform114, the access management system118, the Web proxy120, remote computing device106) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein.

In alternative embodiments, the machine900operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine900may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine900may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a smart phone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions916, sequentially or otherwise, that specify actions to be taken by the machine900. Further, while only a single machine900is illustrated, the term “machine” shall also be taken to include a collection of machines900that individually or jointly execute the instructions916to perform any one or more of the methodologies discussed herein.

The machine900includes processors910, memory930, and input/output (I/O) components950configured to communicate with each other such as via a bus902. In an example embodiment, the processors910(e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor912and a processor914that may execute the instructions916. The term “processor” is intended to include multi-core processors910that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions916contemporaneously. AlthoughFIG. 9shows multiple processors910, the machine900may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof.

The memory930may include a main memory932, a static memory934, and a storage unit936, all accessible to the processors910such as via the bus902. The main memory932, the static memory934, and the storage unit936store the instructions916embodying any one or more of the methodologies or functions described herein. The instructions916may also reside, completely or partially, within the main memory932, within the static memory934, within the storage unit936, within at least one of the processors910(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine900.

Communication may be implemented using a wide variety of technologies. The I/O components950may include communication components964operable to couple the machine900to a network980or devices970via a coupling982and a coupling972, respectively. For example, the communication components964may include a network interface component or another suitable device to interface with the network980. In further examples, the communication components964may include wired communication components, wireless communication components, cellular communication components, and other communication components to provide communication via other modalities. The devices970may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a universal serial bus (USB)). For example, as noted above, the machine900may correspond to any one of the remote computing device106, the access management system110, the compute service manager112, the execution platform114, the access management system118, the Web proxy120, and the devices970may include any other of these systems and devices.

The various memories (e.g.,930,932,934, and/or memory of the processor(s)910and/or the storage unit936) may store one or more sets of instructions916and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions916, when executed by the processor(s)910, cause various operations to implement the disclosed embodiments.

The following numbered examples are embodiments:

Example 1. A method comprising: providing a first party data in a first account; providing a second party data in a second account; executing, by a processor, a secure function using the first party data to generate a first result, including creating links to the first party data and anonymizing identification information in the first party data; sharing the secure function with the second account; executing the secure function using the second party data to generate a second result and restricting the second account from accessing the first party data; and generating a cross reference table with the first and second results, the cross reference table providing anonymized matches of the first and second results.

Example 2. The method of example 1, further comprising: restricting the second account from accessing a code of the secure function.

Example 3. The method of any of examples 1-2, further comprising: restricting the second account from logs related to execution of the first portion of the secure function.

Example 4. The method of any of examples 1-3, further comprising: generating dummy matching information in the second result for an instance of no match.

Example 5. The method of any of examples 1-4, further comprising: generating a summary report of the anonymized matches.

Example 6. The method of any of examples 1-5, further comprising: restricting access to the number of matches when the number of matches is below a minimum threshold.

Example 7. The method of any of examples 1-6, wherein providing the first party data includes: uploading a load file to a secure cloud storage location; storing data from the load file into an enclave account; and setting access restrictions for the data from the load file based on control information.

Example 8. The method of any of examples 1-7, further comprising: receiving a query request; based at least on the first party data and the cross reference table, executing a first portion of the query request; generating an interim table based on executing the first portion of the query request; generating a secure query request, including instructions related to executing a second portion of the query request; and sharing the secure query request and the interim table with the second account.

Example 9. The method of any of examples 1-8, further comprising: at the second account, executing the secure query request and joining results of the secure query requests with information from the interim table to generate final results of the query request.

Example 10. A system comprising: one or more processors of a machine; and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations implementing any one of example methods1to9.

Example 11. A machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations implementing any one of example methods1to9.