Protecting privacy in an online setting

Systems, methods, and computer program products to perform an operation comprising receiving digital content associated with an account identifier, parsing the digital content and extracting a set of attributes from the digital content, receiving via a network connection, from each of a plurality of online identity services, a set of identities matching at least one attribute of the set of attributes, intersecting the sets of identities to create a set of candidate identities, computing a score for each identity in the set of candidate identities, wherein each score reflects a likelihood that the respective candidate identity is associated with the account identifier; and returning a ranked list of the scored candidate identities and an indication of at least one item of evidence linking the respective candidate identity to the account identifier.

BACKGROUND

The present invention relates to computer software, and more specifically, to protecting privacy in an online setting.

Maintaining privacy is difficult when personal information is shared online. While often harmless, sharing personal information on an online platform may pose risks to personal privacy and safety. Many types of data, in isolation, lend little to divulging an online account holder's real-world identity. However, seemingly innocent data points, when used in combination, may uniquely identify a person. For example, research has shown that a significant number of Americans can be uniquely identified by a combination of their birth date, zip code, and gender. Certain groups of users, such as children, are especially at risk when their personal privacy is compromised. These risks remain even though users may post information online through an alias—such as an email address, account name/login, and the like—rather than under their real names. Therefore, there is a need to protect personal privacy in online settings.

SUMMARY

Embodiments disclosed herein provide systems, methods, and computer program products to perform an operation comprising receiving digital content associated with an account identifier, parsing the digital content and extracting a set of attributes from the digital content, receiving via a network connection, from each of a plurality of online identity services, a set of identities matching at least one attribute of the set of attributes, intersecting the sets of identities to create a set of candidate identities, computing a score for each identity in the set of candidate identities, wherein each score reflects a likelihood that the respective candidate identity is associated with the account identifier; and returning a ranked list of the scored candidate identities and an indication of at least one item of evidence linking the respective candidate identity to the account identifier.

DETAILED DESCRIPTION

Embodiments disclosed herein provide techniques to protect privacy in an online setting. In at least one embodiment, a privacy manager may monitor content users intend to publish via an online account, and issue warnings when the content includes information that may be used to identify the person behind the online account to a degree of certainty. In some embodiments, the privacy manager may restrict publication of such content, modify the content, or otherwise alert the user to the risks posed by publishing the content.

For example, online video games often allow users to chat amongst each other in text-based chat rooms. The users may have a unique username, which often does not correspond to their real name. The messages users post in these chat rooms may often be observed not only by their friends, but by any number of other unknown people. For example, a child playing a video game may be unknowingly divulging information that can identify the child, exposing the child to different types of threats to their safety. If the privacy manager determines that the child is composing a message that would uniquely identify the child (or limit the number of total possible matches to a handful of people), the privacy manager may prevent the child from submitting the message to the chat room. The privacy manager may also edit the message to remove those features that are especially relevant in compromising the user's anonymity, such as their home address. Further still, the privacy manager may add features to the message that may significantly increase the number of real-life people that the message can be attributed to.

FIG. 1Aillustrates techniques to protect privacy in an online setting, according to one embodiment. As shown,FIG. 1Adepicts a graphical user interface (GUI)100that users can use to compose messages in a text field101. Generally, users may type into the text field101and click the submit button103to publish the text online, such as in a public forum, online chat room, social media platform, and the like. As shown, a user “SMARTGIRL” has entered detailed text specifying, among other things, her address, the names of her siblings, and how much she enjoys learning from her teacher, Mrs. Krabappel. Because this information may reveal the SMARTGIRL's real life identity, the privacy manager disclosed herein may determine how many real-world people can be attributed to the post. As shown, the privacy manager has analyzed the text of the post, and has outputted a warning104to the user. The privacy manager has also disabled the submit button103, which will prevent the user from submitting the post to the forum. The privacy manager has also provided a security meter106which shows the level of risk in submitting the text composition.

As shown, the warning104indicates that the user cannot submit the post due to the risk to her privacy. The warning104further indicates that the post can only be associated with one person, namely L. Simpson. The warning also provides the attributes that were relevant in resolving the username SMARTGIRL to a single person, in this example the education details, sibling information, and address in the text box101. The popup box105is generated when the user hovers or clicks a mouse on each attribute, and shows, in this example, that L. Simpson lives at 742 Evergreen Terrace.

In one embodiment, the presence of other factors may cause the privacy manager to lessen the severity of a warning. For example, if other individuals were discovered with similar attributes over time, the privacy manager may lessen the severity of a warning. Similarly, if conflicting information is included in the user's history, the warning may be modified due to a lesser likelihood of divulging the identity of the user. For example, if a previous blog post by SMARTGIRL indicates she had no siblings, the privacy manager may take this into account when generating a warning. Further still, if content associated with a user is removed or modified, the privacy manager may remove the warning.

FIG. 1Bdepicts the GUI100after the privacy manager has modified the text in the text box101to reduce the risk to SMARTGIRL's privacy and safety. As shown, the privacy manager has removed information, added information, and modified information in the text, such that the post can now be associated with over 1,000 people. For example, the teacher's name has been removed, along with the names of the siblings. Similarly, the street address been removed. Further still, the hometown city of Springfield has now been replaced with the state of Oregon, which is one of many states that includes the city of Springfield. As another example, the hometown of Springfield may be changed to a different city the person lived in, such as New York. Without the specific attributes inFIG. 1A, the post can be associated with thousands of people, and therefore no longer poses a risk to the user. As shown, due to the lesser security risk, the security meter106is now at a lower level relative to the position inFIG. 1A. Similarly, the submit button103is now enabled, and the user can submit the post to the online forum.

FIG. 1Cdepicts a GUI110that provides online privacy checks for users. Through the GUI110, a user may supply their email address, alias, or other user identifier in the input box111. When the user clicks the submit button112, the privacy manager may determine how many people can be associated with the submitted user ID. As shown, the privacy manager may return an indication of the user's privacy level, such as “high,” “medium,” or “low.” The privacy level may be based on the number of possible names that may be associated with the user ID. For example, if only one result was returned, the privacy manager may output an indication of a “low” privacy level. The privacy manager may also return an ordered list of a subset of results. As shown, the privacy manager has returned the top 5 results (out of over 5,000 results) along with supporting evidence showing why the person can be associated with the input user ID. Therefore, as shown, a “L. Simpson” is the first result, which indicates “L. Simpson” is the person most likely to be associated with the user ID of SMARTGIRL. The privacy manager has also included evidence that was most impactful in matching L. Simpson to the user ID, in this case, the exemplary attributes of hometown, education history, and siblings.

FIG. 2is a flow diagram200illustrating techniques to protect privacy in an online setting, according to one embodiment. Generally, the diagram200depicts a process implemented by the privacy manager to determine how many people can be associated with a user identifier, as well as actions that the privacy manager may trigger in response. As shown, at block201, the privacy manager receives a user identifier (ID) (such as an email address, login, and the like). Continuing with the example fromFIGS. 1A-1C, the user ID would be the username “SMARTGIRL.” At block202, the privacy manager may identify all publications known to have come from the input user ID. For example, the privacy manager may search online forums, product reviews, social media posts/comments, social media publications, or any type of published textual, audio, or video content associated with the input user ID. Generally, audio and/or video may be transcribed and annotated by a natural language processing (NLP) system to generate metadata and/or attributes of a person creating or appearing in the audio or video. Similarly, image processing techniques (such as facial recognition, text recognition) applied to photos or still images from a video may be used to recognize a person and extract attributes or metadata therefrom. In one embodiment, the data extracted at block202includes posts that are being composed for publication, such as the composition101inFIGS. 1A-1B.

At block203, the privacy manager may extract attributes from the content identified at block202. In at least one embodiment, the privacy manager may use natural language processing (NLP) techniques to extract the attributes from the content. The attributes may be personal attributes such as name, age, birthdate, gender, marital status, birthplace, education history, current address, address histories, and the like. Generally, the privacy manager may extract any number and type of attributes. An example set of attributes are depicted in block204. The privacy manager may send one or more of the extracted attributes (and/or the passages of text from which the attributes were extracted, or an associated image or video) to a plurality of different identity services. Generally, an identity service is any online service that maintains a database of information related to people, and can return results based on an input query including one or more of the extracted attributes. As shown, for example, ID service205is a school record database that can return information about students based on age and education history as example input attributes. ID service206, on the other hand, is a county marriage record service, which may receive marital status and location as example input attributes. ID service207represents any number and type of other ID services, such as telephone directories, family ancestry databases, and the like. The ID services may generally return as output a listing of people who match one or more of the attributes. The ID services may further specify a confidence that a given result is associated with the input user ID, and which, if any, attributes matched the person record (and to what degree).

In some embodiments, the identity services may return few results or no results at all. In such embodiments, the privacy manager may try to obtain identifying information from publicly available online sources on the web, which may generally be considered additional identity services. For example, a user identifier (such as email address, username, first name, last name, etc.) in the digital content identified at block202and/or the attributes extracted from the content at block203may be used to search the web for candidate identities that may be associated with the content or user attributes. The privacy manager may then process and score these candidate identities in the same way as candidate identities returned from the identity services.

At block208, the privacy manager may intersect each result set from each ID service205-207to produce a set of candidate identities that were present in each result set returned from the ID services. In most cases, each ID service may return hundreds, if not thousands of records. Intersecting the sets of records may therefore eliminate many records and produce a more manageable data set, such as 25 candidate identities, as the case may be. At block209, the privacy manager may use machine learning (ML) models or other logic/algorithms to compute a confidence score reflecting the likelihood that the input user ID is associated with a given identity in the candidate identities. For example, if the candidate identities include the names of 5 different people, the privacy manager would compute five different confidence scores, where each confidence score reflects the likelihood that the respective person is associated with the user ID. In addition, as reflected at block210, the privacy manager may generate evidence for each confidence score based on the passages of text used by the identity services to match extracted attributes to a record in their respective databases. For example, each ID service may return an indication of an exact match between the attributes of address and gender. The privacy manager may then determine that the address and gender attributes were the most critical in identifying a person corresponding to the input user ID. As another example, the identity service may return an image of a person that matches a person in the image provided to the identity service with the initial search request. The supporting evidence may be presented to the user as part of a result list, such as the evidence depicted inFIG. 1A or 1C.

At block211, the privacy manager may aggregate the results and produce an ordered list of candidate entities that are ranked according to their confidence scores. The list may also include the supporting evidence used to score each candidate entity. The privacy manager may then take any number of additional steps. If, for example, the user is composing a new message (similar toFIG. 1A), the privacy manager at block212may return to block201to re-iterate using the latest text data provided by the user (e.g., the composition101). In doing so, the privacy manager may generate a new result list and compare the results to the previous set of results. For example, the previous set of results (from a first iteration) may include 200 candidate identities, or 200 people that may be creating the content associated with the input user ID. The second set of results, using the current input data, however, may narrow the list of candidate identities to 3 people. Such a dramatic reduction in the size of the list may indicate that the current composition poses a risk to the user's safety and privacy, and may output a warning, or take other steps to restrict publication of the current composition as depicted inFIG. 1A.

At block213, the privacy manager may output a warning for the person's privacy level. Examples of such an indication are depicted inFIGS. 1A and 1C. The privacy manager may output the warning when a specified set of criteria are met. For example, if the aggregated result set generated at block211includes a number of candidate names (2, for example) that is below a threshold (5 names, for example), the warning may be triggered. Similarly, in another embodiment, if the result set based on a current composition reduces the previous result set (without the current composition) by a predefined percentage, the privacy manager may output the warning. At block214, the privacy manager may output a current privacy level and/or an indication of a person associated with the input user ID. In at least one embodiment, such as the example depicted inFIG. 1C, a user may provide their own alias, email address, or other user ID to the privacy manager at block201. The privacy manager may process the user ID according to the flow diagram200to produce an indication of the person's privacy level. The indication may be of any type, such as a numeric privacy score, or a privacy level. Similarly, the privacy manager may output a list of names that may own the user ID.

FIG. 3is a block diagram illustrating a system300configured to protect privacy in an online setting, according to one embodiment. The networked system300includes a computer302. The computer302may also be connected to other computers via a network330. In general, the network330may be a telecommunications network and/or a wide area network (WAN). In a particular embodiment, the network330is the Internet.

The computer302generally includes a processor304which obtains instructions and data via a bus320from a memory306and/or a storage308. The computer302may also include one or more network interface devices318, input devices322, and output devices324connected to the bus320. The computer302is generally under the control of an operating system (not shown). Examples of operating systems include the UNIX operating system, versions of the Microsoft Windows operating system, and distributions of the Linux operating system. (UNIX is a registered trademark of The Open Group in the United States and other countries. Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.) More generally, any operating system supporting the functions disclosed herein may be used. The processor304is a programmable logic device that performs instruction, logic, and mathematical processing, and may be representative of one or more CPUs. The network interface device318may be any type of network communications device allowing the computer302to communicate with other computers via the network330.

The storage308is representative of hard-disk drives, solid state drives, flash memory devices, optical media and the like. Generally, the storage308stores application programs and data for use by the computer302. In addition, the memory306and the storage308may be considered to include memory physically located elsewhere; for example, on another computer coupled to the computer302via the bus320.

The input device322may be any device for providing input to the computer302. For example, a keyboard and/or a mouse may be used. The input device322represents a wide variety of input devices, including keyboards, mice, controllers, and so on. Furthermore, the input device322may include a set of buttons, switches or other physical device mechanisms for controlling the computer302. The output device324may include output devices such as monitors, touch screen displays, and so on.

As shown, the memory306contains the privacy manager312, which is generally configured to protect user privacy in online settings. The privacy manager312may generally perform any number of different operations to protect privacy, such as those described inFIGS. 1A-1B and 2. For example, if a person is about to post a comment on a social media platform that would allow malicious users to identify the person, the privacy manager312may restrict the comment from being posted, modify the post, send parents email alerts, and the like. Generally, by identifying all text publications associated with a user ID in the data sources350, the privacy manager312may extract attributes from the text, query one or more ID services340to receive sets of matching records. The privacy manager312may then intersect the result sets from the ID services340to generate a list of candidate entities. The privacy manager312may then compute a confidence score for each candidate entity, where the confidence score reflects a likelihood that the candidate entity (a person) is associated with the user ID. The privacy manager312may also identify evidence to supplement the confidence scores, as described above. The privacy manager312may then aggregate the results to produce a ranked listing of candidate entities. Based on this algorithm, the privacy manager312may then generate any type of output, such as a warning, restriction, or the ranked listing with evidence (as depicted inFIG. 1A).

As shown, the storage308includes the metadata315, settings316, and models317. The metadata315stores metadata attributes of each of a plurality of user IDs. The attributes may be any type of personal attribute, such as name, age, birthdate, gender, marital status, educational history, and the like. The settings316may include settings for the privacy manager312, such as threshold numbers of results, which, if not exceeded, would generate warnings that personal security is at risk. The ML models317include machine learning (ML) models that the privacy manager312may use to compute confidence scores for each candidate identity. Generally, the machine learning models317specify weights for attributes or features, that are relevant in producing a confidence score for candidate identities. The ML models317may be adapted over time, if, for example, certain attributes are determined to be more relevant in producing an accurate confidence score. For example, a first ML model317may heavily weight the combination of zip code, birth date, and gender as relevant in correctly identifying a person. Therefore, if a candidate entity in the set of candidate entities has a birth date, zip code, and gender that matches those in the metadata315corresponding to the user ID, the first ML model317may produce a score indicating a high likelihood that the candidate entity is associated with the user ID.

As shown, the privacy manager312may access a plurality of data sources350via the network330. The data sources350may be any type of data source that stores user content351, such as forums, social media platforms, blogs, reviews, chat rooms, and the like. The ID services340are online services that can return information regarding people from their respective ID service data sets341. Examples of ID services340include county marriage records, school records, real estate records, social media sites, and the like.

FIG. 4is a flow chart illustrating a method400to protect privacy in an online setting, according to one embodiment. Generally, the privacy manager312may execute the steps of the method400responsive to a user request to determine the level of privacy (or anonymity) of their user ID. As shown, the method begins at step410, where the privacy manager312may receive an account ID, such as an email address, account login name, and the like. At step420, the privacy manager312may identify any content associated with the account ID received at step410. For example, the privacy manager312may search the Internet for available content associated with the user ID in the content351of the data sources350. At step430, the privacy manager312may perform natural language processing to extract attributes from the identified content. For example, the privacy manager312may identify the gender, birth date, and other attributes in the content that may be associated with the person that created the content. At step440, the privacy manager312may query the ID services340using the attributes extracted at step430. The query may request as results any person records in the ID service data341matching the extracted attributes.

At step450, the privacy manager312may receive result sets from each ID service340. For example, a first ID service may return 10,000 possible matches, while a second ID service may return 15,000 possible matches, and a third ID service may return 20,000 possible matches. At step460, the privacy manager312may determine an intersection of the result sets returned by the ID services340to produce a single set of unique results. For example, the privacy manager312may determine that of the 45,000 results returned by the three ID services, 5,000 results were unique (and members in each result set), thereby producing a set of candidate identities. In another embodiment, the privacy manager312may intersect the results using fuzzy-matching and thresholding. For example, a first ID service may return “John Smith” with a confidence of 10%, a second ID service may return “John A. Smith” with a confidence of 12%. In one embodiment, the privacy manager312may include a single entry for the “fuzzy-match” between “John Smith” and “John A. Smith” in the intersected result set (which may include a low confidence based on the confidences returned by the ID services). In another embodiment, the privacy manager312may discard the results due to their confidence scores being below a threshold (for example, 20%). Stated differently, therefore, the privacy manager312may consider whether a confidence score returned by the ID services340exceeds a threshold before including a given result in the intersection of result sets. At step470, the privacy manager312may compute a confidence score for each result in the set of candidate identities (or a subset thereof). Generally, the privacy manager312may use ML models317or other algorithms to score the candidate identities, where the score reflects a likelihood that the candidate identity is the owner of the input user ID. One example method to compute a confidence score is described with reference toFIG. 6, however, any suitable algorithm may be applied. At step480, the privacy manager312may generate evidence for each result in the set of candidate identities. For example, all three ID services may indicate that person X is the most likely match to the provided attributes based on gender, birth date, and address, the privacy manager312may determine that gender, birth date, and address are the most influential pieces of evidence when computing the confidence score, and return these items as supporting evidence with a result set. At step490, the privacy manager312may output an ordered list of results (such as the results depicted inFIG. 1C) along with items of supporting evidence.

FIG. 5is a flow chart illustrating a method500to protect privacy in an online setting, according to one embodiment. Generally, the privacy manager312may implement the steps of the method500to prevent users from divulging information that may jeopardize their privacy. As shown, the method500begins at step510, where the privacy manager312may receive an account ID, such as an email address, account login names, and the like. At step520, the privacy manager312may identify any content associated with the account ID received at step510. The content may include a current composition that the user is preparing to submit for online publication, such as the content in box101ofFIG. 1A. As previously described, the privacy manager312may also search the Internet for available content associated with the user ID in the content351of the data sources350. For example, the privacy manager312may find blog posts, product reviews, social media posts, and the like that are associated with the input user ID. At step530, the privacy manager312may perform natural language processing to extract attributes from the identified content. For example, the privacy manager312may identify the gender, birth date, and other attributes in the content that may be associated with the person that created the content. At step540, the privacy manager312may query the ID services340using the attributes extracted at step530. The query may request as results any person records in the ID service data341matching the extracted attributes.

At step550, the privacy manager312may receive result sets from each ID service340. For example, a first ID service may return 10,000 possible matches, while a second ID service may return 15,000 possible matches, and a third ID service may return 20,000 possible matches. At step560, the privacy manager312may determine an intersection of the result sets returned by the ID services340to produce a single set of unique results. For example, the privacy manager312may determine that of the 45,000 results returned by the three ID services, 5,000 results were unique (and common to each result set), thereby producing a set of candidate identities. At step570, the privacy manager312may compute a confidence score for each result in the set of candidate identities (or a subset thereof). Generally, the privacy manager312may use ML models317or other algorithms to score the candidate identities, where the score reflects a likelihood that the candidate identity is associated with the input user ID. One example method to compute a confidence score is described with reference toFIG. 6, however, any suitable algorithm may be applied. At step580, the privacy manager312may generate evidence for each result in the set of candidate identities. For example, all three ID services may indicate that person Y is the most likely match to the provided attributes based on gender, birth date, and address, the privacy manager312may determine that gender, birth date, and address are the most influential pieces of evidence when computing the confidence score, and return these items as supporting evidence with a result set. At step590, the privacy manager312may output an ordered list of results (such as the results depicted inFIG. 1A or 1C) along with items of supporting evidence.

At step595, the privacy manager312may perform a predefined operation to restrict submission of input upon determining that the number of results is below a predefined threshold. For example, if 10 results is the threshold, and the privacy manager312determines that four people are in the list of candidate entities, the privacy manager312may restrict submission of content that the user is composing. In one embodiment, the privacy manager312performs the predefined operation upon determining that a change in the number of results based on the input exceeds a threshold. For example, a record in the metadata315may indicate that 1,000 people can be associated with a specific email address. However, after iterating through the method500using input the user is composing for online publication, the privacy manager312may determine that only 7 people can be associated with the email address if the new input is considered. Therefore, due to this significant change (either based on percentage or number of results), the privacy manager312may restrict submission of the new content.

FIG. 6is a flow chart illustrating a method600to compute a confidence score for a plurality of unique results, according to one embodiment. As shown, the method600begins at step610, where the privacy manager312executes a loop including steps620-670for each unique identity in the candidate list of identities returned by the ID services340. At step620, the privacy manager312executes a loop including steps630-640for each metadata attribute specified as matching (to any degree) the attributes of records in the ID service data341. For example, the metadata attributes extracted from content using NLP techniques may indicate the person associated with a user ID lives in New York City and is 50 years old. A first ID service may return person X as being an 80% match for living in New York City (possibly because the person lives in Brooklyn), and a 90% match on age because person X is known to be within the age range of 49-50. Similarly, the extracted metadata may indicate that the person associated with the user ID is female, while person X is a male. The privacy manager312may then provide these attributes, whether they match, and their corresponding percentages as input into an algorithm or ML model317at step630. At step640, the privacy manager312determines whether any additional metadata attributes remain. If additional metadata attributes remain, the privacy manager312returns to step620. Otherwise, the privacy manager312proceeds to step650. At step650, the privacy manager312may provide an indication of an overall match between the user ID and a record as input to an algorithm or ML model. For example, the ID services340may also return an overall score indicating how well a given record matches to the provided attributes (and therefore the user ID). For example, record 1 may have an 85% overall match to a user ID, while record 2 may have a 50% overall match to the user ID. These values may be inputted to the ML models317and/or the algorithms. At step660, the privacy manager312may compute a confidence score reflecting the likelihood that the unique identity in the candidate list of entities is associated with the user ID based on the input provided to the algorithm and/or ML models317at steps630and650. At step670, the privacy manager312may determine whether more identities remain in the candidate list of entities. If more identities remain, the privacy manager312returns to step610to compute a confidence score for the remaining identities. Otherwise, the method600ends.

FIG. 7is a flow chart illustrating a method700to perform a predefined operation to restrict submission of input, according to one embodiment. Generally, the privacy manager312may implement any number and type of operations to restrict submission of input that could jeopardize a user's privacy in online settings. As shown, the method700begins at step710, where the privacy manager312may optionally modify text that includes sensitive information. For example, if a user included their social security number in a blog post, the privacy manager312may remove the social security number, blur out the social security number, add a false social security number (such as one with 15 digits) and the like. At step720, the privacy manager312may optionally restrict submission of input by the user. For example, the privacy manager312may disable submit buttons, disable network connections, and the like. At step730, the privacy manager312may optionally provide warnings via a user interface to alert the user. Examples of such warnings are depicted inFIG. 1A. At step740, the privacy manager312may optionally transmit an external notification, such as an email alert or a smartphone alert to a parent when their child is attempting to post content that may jeopardize their privacy.

Advantageously, embodiments disclosed herein provide techniques to help people from divulging information that may jeopardize their privacy. For example, users may be restricted from posting content that may uniquely identify them on the Internet. Similarly, users can query the privacy manager to determine their current privacy level, where the privacy level is based on the number of unique people (zero to potentially billions) that can be associated with a given user ID.

Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g. an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present invention, a user may access applications or related data available in the cloud. For example, the privacy manager312could execute on a computing system in the cloud and compute a privacy level for a given user ID. In such a case, the privacy manager could store the privacy level at a storage location in the cloud. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet).