Systems and methods for identifying privacy leakage information

The disclosed computer-implemented method for identifying privacy leakage information may include (1) identifying, at the computing device, at least one informative word in a digital text and (2) performing a security action that identifies privacy leakage information, where the security action includes (A) determining, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage, (B) determining, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display, and (C) displaying, on a display device, the combined confidence score for each respective element within the level of detail to display. Various other methods, systems, and computer-readable media are also disclosed.

BACKGROUND

When writing text, authors may provide information that inadvertently reveals more personal information than intended. For example, while writing an email, users may use words that appear innocuous but in fact may be quite telling. Such mistakes may be more than merely embarrassing, as revealing sensitive information may subsequently expose users to cyberattacks. Additionally, certain personal information, such as medical information, may in many locations be prohibited from being released. While someone knowing this information may be able to avoid intentional releases of the information, inadvertent releases may occur due to the use of certain words. For example, the term “gp120” refers to a protein associated with HIV. Use of the term “gp120” in an email may reveal just as much medical information as the word “HIV” itself does and should be used with caution or avoided. The instant disclosure, therefore, identifies and addresses a need for systems and methods for identifying privacy leakage information.

SUMMARY

As will be described in greater detail below, the instant disclosure describes various systems and methods for identifying privacy leakage information.

In one example, a method for identifying privacy leakage information may include (i) identifying, at the computing device, at least one informative word in digital text and (ii) performing a security action that identifies privacy leakage information, where the security action includes (A) determining, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage, (B) determining, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display, and (C) displaying, on a display device, the combined confidence score for each respective element within the level of detail to display.

In one example, the method may further include receiving, from a graphical user interface, an input indicating the level of detail to display.

In some examples, the digital text may be in a set of electronic documents.

In some embodiments, the level of detail to display may be in a hierarchy of detail including (i) a document level of detail, (ii) a sentence level of detail, and (iii) a word level of detail. In an embodiment, the level of detail to display may be a document level of detail and each respective element may be a document. In one example, the level of detail to display may be a sentence level of detail and each respective element may be a sentence. In some examples, the level of detail to display may be a word level of detail and each respective element may be a word.

In some embodiments, the determining the combined confidence score for each respective element may further include (i) adding respective confidence scores for each type of privacy leakage for each identified informative word in the respective element, (ii) normalizing the sum of respective confidence scores, and (iii) sorting the normalized sum.

In an embodiment, the displaying may further include determining a two-way tree-structured index including (i) the combined confidence score for each respective element within each of the levels of detail and (ii) associations between related elements across different levels of detail. In one example, the displaying may further include identifying the combined confidence score to display by (i) receiving an input indicating a requested level of detail to display, (ii) retrieving, based on the received input, the combined confidence score from the two-way tree-structured index, and (iii) displaying, on the display device, the retrieved combined confidence score.

In some examples, the displaying may further include determining a two-way tree-structured index including (i) a first list of types of privacy leakage, ranked by respective confidence scores, for each respective element within each of the levels of detail, (ii) respective confidence scores for each type of privacy leakage in the first list, (iii) a second list of subtypes of privacy leakage, ranked by respective confidence scores, for each respective type of privacy leakage in the second list, (iv) respective confidence scores for each subtype of privacy leakage in the first list, (v) the respective elements within each of the levels of detail, and (vi) associations between related elements across different levels of detail. In some embodiments, the displaying may further include identifying per-element privacy leakage detail to display by (i) receiving an input indicating a requested level of per-element privacy leakage detail to display, (ii) retrieving, from the two-way tree-structured index and based on the received input, per-element privacy leakage detail and respective confidence scores from at least one of the first list and the second list within the requested level of detail to display, and (iii) displaying, on the display device, the retrieved per-element privacy leakage detail and the respective confidence scores.

In an embodiment, the security action may further include replacing the at least one informative word in the digital text with at least one replacement word.

In one embodiment, a system for identifying privacy leakage information may include at least one physical processor and physical memory that includes computer-executable instructions that, when executed by the physical processor, cause the physical processor to (i) identify, at the system, at least one informative word in digital text and (2ii perform a security action that identifies privacy leakage information, where the security action includes (A) determining, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage, (B) determining, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display, and (C) displaying, on a display device, the combined confidence score for each respective element within the level of detail to display.

In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, may cause the computing device to (i) identify, at the computing device, at least one informative word in digital text and (ii) perform a security action that identifies privacy leakage information, where the security action includes (A) determining, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage, (B) determining, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display, and (C) displaying, on a display device, the combined confidence score for each respective element within the level of detail to display.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The present disclosure is generally directed to systems and methods for identifying privacy leakage information. In some examples, the provided systems and methods may measure top-K privacy leakages (PLs) with confidence scores and enable “zoom-in” and “zoom-out” features for displaying privacy leakages across various different levels of data detail (words, sentences, etc.) and across various different levels of sensitive topic detail.

In some embodiments, privacy leakage may involve personal information and/or sensitive information that may be shared with websites and/or applications other than the websites and/or applications to which that personal information was initially provided. For example, privacy leakage may occur as a result of sending digital text in a form of emails, text messages, chat messages, and/or web browser messaging via non-private networks such as the Internet.

In some embodiments, the provided techniques may include systems to detect privacy leakage for unstructured data such as documents. In some examples, for sets of documents, the provided techniques may detect privacy leakage at various different levels of data detail, such as detecting privacy leakage across at least one document, at least one sentence within a document, at least one word within documents, at least one word within sentences, the like, or a combination thereof. In an example, a hierarchy of level of data detail may be: documents↔<-->document<-->sentences<-->word. In some examples, the provided techniques may detect privacy leakage at various different levels of sensitive topic details. The systems may have drag and drop interfaces where at least one document may be dropped for analysis. The systems may depict top-k privacy leakages for each document, along with associated confidence scores.

If users are interested in exploring (i.e., “zooming-in”) further into a particular document, then users may select that document and the system may show sentences and corresponding top-k privacy leakages for each sentence, along with respective confidence scores. Users may “zoom-in” further into a sentence or set of sentences to understand effects of individual words on the top-k privacy leakages. Once zoomed-in, the system may enable users to “zoom-out” into analysis of coarser levels of detail of data. Similarity, for a particular level of detail of data, users may choose to “zoom-in” into a particular privacy leakage to discover more information about subtopics and “zoom-out” to coarser levels of detail about subtopics. For example, if users want to investigate one level deeper into a particular sensitive topic, say political ideology, then the provided systems and methods analyze privacy leakages at one level of detail deeper into subtopics such as liberal, conservative, moderate, apolitical, etc. In some examples, the provided techniques may provide confidence scores of detected privacy leakage at different data levels of detail and/or different sensitive topic levels of detail.

In some examples, provided are (i) techniques to calculate privacy leakage of sentences and documents using privacy leakage of most informative words, (ii) a privacy leakage (PL) index that enables depicting privacy leakage at different levels of data detail (i.e., granularity), (iii) techniques for providing depicting privacy sensitiveness at different levels of detail, and/or (iv) techniques for providing depicting privacy leakage at different levels of detail based on a public corpus analysis (e.g., digital information available from the Internet).

By doing so, the systems and methods described herein may improve the functionality of a computing device and/or provide targeted protection against privacy leakage, and thus improve fields of privacy protection in general, by providing methods for automatically identifying and/or mitigating privacy leakage in digital text. Examples of the provided techniques improve a state of security of computing devices from which sensitive information may be gleaned (e.g., network-connected devices), resulting in retaining privacy of sensitive information, significant time savings, and/or significant monetary savings. Thus, the disclosed systems and methods may provide asset protection for common targets of anti-privacy attacks, such as home networks, IoT networks, hospitals, shipping companies, financial companies, governments, etc.

The following will provide, with reference toFIGS. 1-2, detailed descriptions of example systems for identifying privacy leakage information. Detailed descriptions of corresponding computer-implemented methods will also be provided in connection withFIG. 3. Detailed descriptions of example implementations will also be provided in connection withFIGS. 4-7. In addition, detailed descriptions of an example computing system and network architecture capable of implementing one or more of the embodiments described herein will be provided in connection withFIGS. 8 and 9, respectively.

FIG. 1is a block diagram of an example system100for identifying privacy leakage information. As illustrated in this figure, example system100may include one or more modules102for performing one or more tasks. As will be explained in greater detail below, modules102may include an identifying module104, a performing module106, a first determining module108, a second determining module110, and/or a displaying module112. Although illustrated as separate elements, one or more of modules102inFIG. 1may represent portions of a single module or application.

As illustrated inFIG. 1, example system100may also include one or more tangible storage devices, such as storage device120. Storage device120generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer-readable instructions. In one example, storage device120may store, load, and/or maintain information indicating one or more of characteristics of an informative word121, a digital text122, a security action123, privacy leakage information124, a type of privacy leakage125, a confidence score126, a probability127that an identified informative word causes a type of privacy leakage, a combined confidence score128, an element129, and/or a level of detail to display130. In some examples, storage device120may generally represent multiple storage devices. Examples of storage device120include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches, a cloud-based storage device, variations or combinations of one or more of the same, and/or any other suitable storage memory.

As illustrated inFIG. 1, example system100may also include one or more physical processors, such as physical processor150. Physical processor150generally represents any type or form of hardware-implemented processing unit capable of interpreting and/or executing computer-readable instructions. In one example, physical processor150may access and/or modify one or more of modules102stored in memory140. Additionally or alternatively, physical processor150may execute one or more of modules102to facilitate identifying privacy leakage information. Examples of physical processor150include, without limitation, microprocessors, microcontrollers, Central Processing Units (CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcore processors, Application-Specific Integrated Circuits (ASICs), portions of one or more of the same, variations or combinations of one or more of the same, and/or any other suitable physical processor.

As illustrated inFIG. 1, example system100may also include one or more network adapters, such as network adapter160. In some examples, network adapter160may be a physical network adapter connected to a physical network (e.g., network204inFIG. 2).

As illustrated inFIG. 1, example system100may also include one or more display devices, such as display170. Display170generally represents any type or form of device capable of visually displaying information (e.g., to a user). In some examples, display170may present a graphical user interface (e.g., to enable user interaction with system100). In non-limiting examples, display170may present at least a portion of informative word121, digital text122, security action123, privacy leakage information124, type of privacy leakage125, confidence score126, probability127that an identified informative word causes a type of privacy leakage, combined confidence score128, element129, and/or level of detail to display130.

Example system100inFIG. 1may be implemented in a variety of ways. For example, all or a portion of example system100may represent portions of example system200inFIG. 2. As shown inFIG. 2, system200may include a computing device202in communication with a server206via a network204. In one example, all or a portion of the functionality of modules102may be performed by computing device202, server206, and/or any other suitable computing system. As will be described in greater detail below, one or more of modules102fromFIG. 1may, when executed by at least one processor of computing device202and/or server206, enable computing device202and/or server206to identify privacy leakage information. For example, and as will be described in greater detail below, one or more of modules102may cause computing device202and/or server206to (i) identify, at system100, at least one informative word121in digital text122and (ii) perform security action123that identifies privacy leakage information124, where security action123may include (A) determining, for at least one identified informative word121, type of privacy leakage125and respective confidence score126indicating probability127the identified informative word121causes the type of privacy leakage125, (B) determining, using the respective confidence score126, combined confidence score126for each respective element129within level of detail to display130, and (C) displaying, on display170, the combined confidence score128for each respective element129within the level of detail to display130.

Computing device202generally represents any type or form of computing device capable of reading computer-executable instructions. In some examples, computing device202may represent a computer running security software, such as anti-malware software and/or privacy software. Additional examples of computing device202include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), smart vehicles, smart packaging (e.g., active or intelligent packaging), gaming consoles, so-called Internet-of-Things devices (e.g., smart appliances, etc.), variations or combinations of one or more of the same, and/or any other suitable computing device.

Server206generally represents any type or form of computing device capable of reading computer-executable instructions. In some examples, server206may represent a computer running security software, such as anti-malware software and/or privacy software. Additional examples of server206include, without limitation, security servers, application servers, web servers, storage servers, and/or database servers configured to run certain software applications and/or provide various security, web, storage, and/or database services. Although illustrated as a single entity inFIG. 2, server206may include and/or represent a plurality of servers that work and/or operate in conjunction with one another.

As illustrated inFIG. 3, at step302one or more of the systems described herein may identify (e.g., at a computing device such as system100), at least one informative word (i.e., sensitive word) in digital text. The systems described herein may perform step302in a variety of ways. For example, identifying module104may, as part of computing device202and/or server206inFIG. 2, identify, at system100, at least one informative word121in digital text122.

In some examples, the digital text may be in a set of electronic documents. In one example, the digital text may be unstructured data. In some examples, a drag-and-drop feature may enable selecting at least one document for processing by method300. In some examples, a drag-and-drop feature may enable selecting at least one sentence for processing by method300.

In some examples, the provided techniques may perform part of speech (POS) tagging and may extract proper nouns and named entities to identify informative words. For example, in the example sentence (Sj)=“Work is going on well and for the long weekend, I cooked Baklava,” the words “baklava” and “weekend” are the most informative words as the word baklava is a named entity and weekend is a proper noun.

In some examples, method300may be executed by and/or from a plug-in within a messaging application, an email plug-in, a chat plug-in, a browser extension, the like, or a combination thereof executing on system100, but in general any words, phrases, or other terms may be analyzed for privacy leakage risks.

In some examples, informative words may be displayed on a display and may be highlighted for ease of reference by users.

As illustrated inFIG. 3, at step304one or more of the systems described herein may perform a security action that identifies privacy leakage information. The security action may include step306, step308, and/or step310. The systems described herein may perform step304in a variety of ways. For example, performing module106may, as part of computing device202and/or server206inFIG. 2, perform security action123that identifies privacy leakage information124. The security action123may include step306, step308, and/or step310.

In an embodiment, the security action may further include replacing the at least one informative word in the digital text with at least one replacement word.

In some examples, security action123may be performed according to data loss prevention (DLP) policies. Example security actions may include blocking access to devices (e.g., storage devices, memories, network devices, etc.), allowing limited access to devices, allowing read-only access to devices, encrypting information, and/or other acts limiting access to devices. In some examples, security actions may be performed automatically. In some embodiments, security actions may be performed based on a level of sensitivity of information that executing processes may attempt to transfer.

In some embodiments, security actions may attempt to identify and/or ameliorate potential security risks posed by processes executing in containers. In some examples, security actions many include blocking access to and/or by the executing processes. In additional examples, the security actions may include displaying, on user displays, warnings indicating that processes are potentially dangerous. In some examples, the security actions may further include allowing the processes access to information that only authenticated processes may access.

As illustrated inFIG. 3, at step306one or more of the systems described herein may determine, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage. The systems described herein may perform step306in a variety of ways. For example, first determining module108may, as part of computing device202and/or server206inFIG. 2, determine, for at least one identified informative word121, type of privacy leakage125and respective confidence score126indicating probability127the identified informative word121causes the type of privacy leakage125.

We turn now toFIG. 4, which includes a diagram400of an example sensitive word, example respective sensitive topics, and example respective confidence scores. On each identified informative word, the provided techniques find an association between the identified informative word and respective sensitive topics with corresponding probable privacy leakage confidence scores.FIG. 4depicts an example of informative word “Baklava” that is associated with respective sensitive topics shown on the right. In this example, the sensitive topics are sorted in order of privacy leakage confidence scores. For example,FIG. 4depicts that the word “Baklava” leaks sensitive topic information about ethnic origin with probability or confidence 0.341.

We now return toFIG. 3. In some examples, the number of privacy leakage topics for a respective word may be user-determined. For example, method300may include receiving a user input (e.g., k=4) indicating that privacy leakage topics having the four highest confidence scores are to be identified and/or subsequently processed for at least one identified informative word.

As illustrated inFIG. 3, at step308one or more of the systems described herein may determine, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display. The systems described herein may perform step308in a variety of ways. For example, second determining module110may, as part of computing device202and/or server206inFIG. 2, determine, using the respective confidence score126, combined confidence score128for each respective element129within level of detail to display130.

In one example, method300may include receiving, from a graphical user interface, an input indicating the level of detail to display. In some examples, the level of detail to display may be in a hierarchy of detail including (i) a document level of detail, (ii) a sentence level of detail, and/or (iii) a word level of detail. In an embodiment, the level of detail to display may be a document level of detail and each respective element may be a document. In one example, the level of detail to display may be a sentence level of detail and each respective element may be a sentence. In some examples, the level of detail to display may be a word level of detail and each respective element may be a word. In some examples, the level of detail to display may be in a hierarchy of topical detail including (i) topic level of detail, (ii) a subtopic level of detail, and/or (iii) a sub-subtopic level of detail.

In some embodiments, the determining the combined confidence score for each respective element may further include (i) adding respective confidence scores for each type of privacy leakage for each identified informative word in the respective element, (ii) normalizing the sum of respective confidence scores, and/or (iii) sorting the normalized sum.

As illustrated inFIG. 3, at step310one or more of the systems described herein may display, on a display device (e.g., display170), the combined confidence score for each respective element within the level of detail to display. The systems described herein may perform step310in a variety of ways. For example, displaying module112may, as part of computing device202and/or server206inFIG. 2, display (e.g., on display170) the combined confidence score128for each respective element129within the level of detail to display130.

In some examples, informative words may be displayed on a graphical user interface on a display and may be highlighted for ease of reference by users. In some embodiments, when a cursor may be located substantially near an informative word displayed on a graphical user interface, privacy leakage details (e.g., privacy leakage topics and/or respective confidence scores) may be displayed on the graphical user interface.

In some embodiments, PL(w) may be a privacy leakage of an individual word “w” and a set of informative words in a sentence “Sj” may be identified, and the privacy leakage of sentence Sj, may be calculated as a function of privacy leakages of individual informative words within that sentence. In some embodiments, Direpresents a document, Sjrepresents a sentence, and wkrepresents a word. Thus, privacy leakage of a word wi=PL(wi). Further, If {w|wϵSj} is a set of words that belong to a sentence Sjthen PL(Sj)=f({PL(w)|wϵSj}) where f( )=Sort(Norm(Sum({PLs}))). Privacy leakage of a document may be calculated as well, once privacy leakage of constituent sentences within the document are calculated. Thus, privacy leakage of a document “Di” may be determined as PL(Di)=Sort(Norm(Sum({PL(Sj)|SjϵDi}))).

We turn now toFIG. 5, which includes a diagram500of an example sentence having example sensitive words, example respective sensitive topics, and example respective confidence scores. The example sentence (Sj) is “Work is going on well and for the long weekend, I cooked baklava” and the identified informative words are “weekend” and “baklava.” Thus, using the privacy leakage information for words “Baklava” and “Weekend” shown inFIG. 5, the provided techniques may determine a combined confidence score for identified privacy leakage (PL) topics for the example sentence by adding the confidence scores of corresponding sensitive topics, then normalize the sum, and sorting in descending order of final confidence scores. For example, PL(Sj)=Sort(Norm(Sum(PL(“baklava”)+(“weekend”))). The diagram500also identifies the top four (i.e., k=4) privacy leakage topics of this sentence. We now return toFIG. 3.

In an embodiment, the displaying may further include determining a two-way tree-structured index including (i) the combined confidence score for each respective element within each of the levels of detail and (ii) associations between related elements across different levels of detail. In one example, the displaying may further include identifying the combined confidence score to display by (i) receiving an input indicating a requested level of detail to display, (ii) retrieving, based on the received input, the combined confidence score from the two-way tree-structured index, and/or (iii) displaying (e.g., on display170) the retrieved combined confidence score.

We turn now toFIG. 6, which includes a diagram600of an example privacy leakage index and example levels of detail. In one example, the provided techniques use recursive definitions of privacy leakage to build a two-way tree-structured index (e.g., PL-Index), where data associations and relationship between privacy leakages may be explicitly maintained. For example, associations between most informative words and sentences and in-turn associations between sentences and document to which they belong may be maintained. Most informative words and corresponding privacy leakages form the leaves or base of this tree shaped index. There may be multiple roots to this index, where each root may be an individual document (the example ofFIG. 6depicts only one document). When querying privacy leakage of a particular document, the query may be propagated to sentence-level nodes to calculate sentence level privacy leakages and from there the query for each sentence level privacy leakage may be propagated to word-level nodes. The results are combined to form privacy leakage of a document. During this computation, each privacy leakage may be indexed at a corresponding node to enable zooming-in and zooming-out to display a level of detail. We now return toFIG. 3.

In some examples, the displaying may further include determining a two-way tree-structured index including (i) a first list of types of privacy leakage, ranked by respective confidence scores, for each respective element within each of the levels of detail, (ii) respective confidence scores for each type of privacy leakage in the first list, (iii) a second list of subtypes of privacy leakage, ranked by respective confidence scores, for each respective type of privacy leakage in the second list, (iv) respective confidence scores for each subtype of privacy leakage in the first list, (v) the respective elements within each of the levels of detail, and/or (vi) associations between related elements across different levels of detail. In some embodiments, the displaying may further include identifying per-element privacy leakage detail to display by (i) receiving an input indicating a requested level of per-element privacy leakage detail to display, (ii) retrieving, from the two-way tree-structured index and based on the received input, per-element privacy leakage detail and respective confidence scores from at least one of the first list and the second list within the requested level of detail to display, and/or (iii) displaying (e.g., on display170) the retrieved per-element privacy leakage detail and the respective confidence scores.

We turn now toFIGS. 5 and 7.FIG. 7is a diagram700of example sensitive topics at example levels of detail.FIG. 5depicts example top-four privacy leakages for our example sentence (“Emotional Characteristics”: 0.213, “Ethnic Origin”: 0.175, “Financial Status”: 0.136, “Health”: 0.113). When a user wants to zoom-in into “Ethnic Origin” topics to understand privacy leakage at one level deeper level, then the system identifies “Caucasians”, “Pacific Islanders”, “Asians”, and “Latinos” as possible subtopics, and calculates respective confidence scores of these subtopics “Caucasians”: 0.3, “Pacific Islanders”: 0.05, “Asians”: 0.6, “Latinos”: 0.05. Note that since sentence Sjhas the keyword “Baklava”, PL(Sj) has “Ethnic Origin” as a type of privacy leakage (i.e. a privacy leakage topic), when user requests a higher level of detail, the systems identify “Asians” as a major privacy leakage topic because subtopic “Asians” has a high relative confidence score. From this subtopic, when the user requests a higher level of detail, the system reveals “Turkey” as the major private information leaked because sub-subtopic “Turkey” has a high relative confidence score.

As detailed above, the steps outlined in method300inFIG. 3may enable identifying privacy leakage information. For example, the systems described herein may measure top-K privacy leakages with confidence scores and enable “zoom-in” and “zoom-out” features for displaying privacy leakages across various different levels of data detail (words, sentences, etc.) and across different levels of sensitive topic detail. By doing so, the systems and methods described herein may improve functioning of a computing device and/or provide targeted protection against privacy leakage, and thus improve fields of privacy protection in general, by providing a method for automatically identifying privacy leakage in digital text. Examples of the provided techniques improve a state of security of computing devices from which sensitive information may be gleaned (e.g., network-connected devices), resulting in retaining privacy of sensitive information.

Computing system810broadly represents any single or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system810include, without limitation, workstations, laptops, client-side terminals, servers, distributed computing systems, handheld devices, or any other computing system or device. In its most basic configuration, computing system810may include at least one processor814and a system memory816.

Processor814generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions. In certain embodiments, processor814may receive instructions from a software application or module. These instructions may cause processor814to perform the functions of one or more of the example embodiments described and/or illustrated herein.

System memory816generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or other computer-readable instructions. Examples of system memory816include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory device. Although not required, in certain embodiments computing system810may include both a volatile memory unit (such as, for example, system memory816) and a non-volatile storage device (such as, for example, primary storage device832, as described in detail below). In one example, one or more of modules102fromFIG. 1may be loaded into system memory816.

In some examples, system memory816may store and/or load an operating system840for execution by processor814. In one example, operating system840may include and/or represent software that manages computer hardware and software resources and/or provides common services to computer programs and/or applications on computing system810. Examples of operating system840include, without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS MOBILE, MAC OS, APPLE'S10S, UNIX, GOOGLE CHROME OS, GOOGLE'S ANDROID, SOLARIS, variations of one or more of the same, and/or any other suitable operating system.

In certain embodiments, example computing system810may also include one or more components or elements in addition to processor814and system memory816. For example, as illustrated inFIG. 8, computing system810may include a memory controller818, an Input/Output (I/O) controller820, and a communication interface822, each of which may be interconnected via a communication infrastructure812. Communication infrastructure812generally represents any type or form of infrastructure capable of facilitating communication between one or more components of a computing device. Examples of communication infrastructure812include, without limitation, a communication bus (such as an Industry Standard Architecture (ISA), Peripheral Component Interconnect (PCI), PCI Express (PCIe), or similar bus) and a network.

Memory controller818generally represents any type or form of device capable of handling memory or data or controlling communication between one or more components of computing system810. For example, in certain embodiments memory controller818may control communication between processor814, system memory816, and I/O controller820via communication infrastructure812.

I/O controller820generally represents any type or form of module capable of coordinating and/or controlling the input and output functions of a computing device. For example, in certain embodiments I/O controller820may control or facilitate transfer of data between one or more elements of computing system810, such as processor814, system memory816, communication interface822, display adapter826, input interface830, and storage interface834.

As illustrated inFIG. 8, computing system810may also include at least one display device824coupled to I/O controller820via a display adapter826. Display device824generally represents any type or form of device capable of visually displaying information forwarded by display adapter826. Similarly, display adapter826generally represents any type or form of device configured to forward graphics, text, and other data from communication infrastructure812(or from a frame buffer, as known in the art) for display on display device824.

As illustrated inFIG. 8, example computing system810may also include at least one input device828coupled to I/O controller820via an input interface830. Input device828generally represents any type or form of input device capable of providing input, either computer or human generated, to example computing system810. Examples of input device828include, without limitation, a keyboard, a pointing device, a speech recognition device, variations or combinations of one or more of the same, and/or any other input device.

Additionally or alternatively, example computing system810may include additional I/O devices. For example, example computing system810may include I/O device836. In this example, I/O device836may include and/or represent a user interface that facilitates human interaction with computing system810. Examples of I/O device836include, without limitation, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanner, a microphone, a touchscreen device, variations or combinations of one or more of the same, and/or any other I/O device.

Communication interface822broadly represents any type or form of communication device or adapter capable of facilitating communication between example computing system810and one or more additional devices. For example, in certain embodiments communication interface822may facilitate communication between computing system810and a private or public network including additional computing systems. Examples of communication interface822include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface. In at least one embodiment, communication interface822may provide a direct connection to a remote server via a direct link to a network, such as the Internet. Communication interface822may also indirectly provide such a connection through, for example, a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.

In certain embodiments, communication interface822may also represent a host adapter configured to facilitate communication between computing system810and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communication interface822may also allow computing system810to engage in distributed or remote computing. For example, communication interface822may receive instructions from a remote device or send instructions to a remote device for execution.

In some examples, system memory816may store and/or load a network communication program838for execution by processor814. In one example, network communication program838may include and/or represent software that enables computing system810to establish a network connection842with another computing system (not illustrated inFIG. 8) and/or communicate with the other computing system by way of communication interface822. In this example, network communication program838may direct the flow of outgoing traffic that is sent to the other computing system via network connection842. Additionally or alternatively, network communication program838may direct the processing of incoming traffic that is received from the other computing system via network connection842in connection with processor814.

Although not illustrated in this way inFIG. 8, network communication program838may alternatively be stored and/or loaded in communication interface822. For example, network communication program838may include and/or represent at least a portion of software and/or firmware that is executed by a processor and/or Application Specific Integrated Circuit (ASIC) incorporated in communication interface822.

As illustrated inFIG. 8, example computing system810may also include a primary storage device832and a backup storage device833coupled to communication infrastructure812via a storage interface834. Storage devices832and833generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions. For example, storage devices832and833may be a magnetic disk drive (e.g., a so-called hard drive), a solid state drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash drive, or the like. Storage interface834generally represents any type or form of interface or device for transferring data between storage devices832and833and other components of computing system810. In one example, storage device120fromFIG. 1may be stored and/or loaded in primary storage device832.

In certain embodiments, storage devices832and833may be configured to read from and/or write to a removable storage unit configured to store computer software, data, or other computer-readable information. Examples of suitable removable storage units include, without limitation, a floppy disk, a magnetic tape, an optical disk, a flash memory device, or the like. Storage devices832and833may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system810. For example, storage devices832and833may be configured to read and write software, data, or other computer-readable information. Storage devices832and833may also be a part of computing system810or may be a separate device accessed through other interface systems.

The computer-readable medium containing the computer program may be loaded into computing system810. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory816and/or various portions of storage devices832and833. When executed by processor814, a computer program loaded into computing system810may cause processor814to perform and/or be a means for performing the functions of one or more of the example embodiments described and/or illustrated herein. Additionally or alternatively, one or more of the example embodiments described and/or illustrated herein may be implemented in firmware and/or hardware. For example, computing system810may be configured as an Application Specific Integrated Circuit (ASIC) adapted to implement one or more of the example embodiments disclosed herein.

FIG. 9is a block diagram of an example network architecture900in which client systems910,920, and930and servers940and945may be coupled to a network950. As detailed above, all or a portion of network architecture900may perform and/or be a means for performing, either alone or in combination with other elements, one or more of the steps disclosed herein (such as one or more of the steps illustrated inFIG. 3). All or a portion of network architecture900may also be used to perform and/or be a means for performing other steps and features set forth in the instant disclosure.

Client systems910,920, and930generally represent any type or form of computing device or system, such as example computing system810inFIG. 8. Similarly, servers940and945generally represent computing devices or systems, such as application servers or database servers, configured to provide various database services and/or run certain software applications. Network950generally represents any telecommunication or computer network including, for example, an intranet, a WAN, a LAN, a PAN, or the Internet. In one example, client systems910,920, and/or930and/or servers940and/or945may include all or a portion of system100fromFIG. 1.

As illustrated inFIG. 9, one or more storage devices960(1)-(N) may be directly attached to server940. Similarly, one or more storage devices970(1)-(N) may be directly attached to server945. Storage devices960(1)-(N) and storage devices970(1)-(N) generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions. In certain embodiments, storage devices960(1)-(N) and storage devices970(1)-(N) may represent Network-Attached Storage (NAS) devices configured to communicate with servers940and945using various protocols, such as Network File System (NFS), Server Message Block (SMB), or Common Internet File System (CIFS).

Servers940and945may also be connected to a Storage Area Network (SAN) fabric980. SAN fabric980generally represents any type or form of computer network or architecture capable of facilitating communication between a plurality of storage devices. SAN fabric980may facilitate communication between servers940and945and a plurality of storage devices990(1)-(N) and/or an intelligent storage array995. SAN fabric980may also facilitate, via network950and servers940and945, communication between client systems910,920, and930and storage devices990(1)-(N) and/or intelligent storage array995in such a manner that devices990(1)-(N) and array995appear as locally attached devices to client systems910,920, and930. As with storage devices960(1)-(N) and storage devices970(1)-(N), storage devices990(1)-(N) and intelligent storage array995generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions.

In certain embodiments, and with reference to example computing system810ofFIG. 8, a communication interface, such as communication interface822inFIG. 8, may be used to provide connectivity between each client system910,920, and930and network950. Client systems910,920, and930may be able to access information on server940or945using, for example, a web browser or other client software. Such software may allow client systems910,920, and930to access data hosted by server940, server945, storage devices960(1)-(N), storage devices970(1)-(N), storage devices990(1)-(N), or intelligent storage array995. AlthoughFIG. 9depicts the use of a network (such as the Internet) for exchanging data, the embodiments described and/or illustrated herein are not limited to the Internet or any particular network-based environment.

In at least one embodiment, all or a portion of one or more of the example embodiments disclosed herein may be encoded as a computer program and loaded onto and executed by server940, server945, storage devices960(1)-(N), storage devices970(1)-(N), storage devices990(1)-(N), intelligent storage array995, or any combination thereof. All or a portion of one or more of the example embodiments disclosed herein may also be encoded as a computer program, stored in server940, run by server945, and distributed to client systems910,920, and930over network950.

As detailed above, computing system810and/or one or more components of network architecture900may perform and/or be a means for performing, either alone or in combination with other elements, one or more steps of an example method for identifying privacy leakage information.

In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. For example, one or more of the modules recited herein may receive digital text to be transformed, transform the digital text, output a result of the transformation to a display, use the result of the transformation to trigger a security action, and/or store the result of the transformation to a storage device. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.