Determining an exposure rating to help prevent fraud risk from user data exposure

Disclosed are techniques for providing an exposure rating. In an aspect, a processing system provides, to a user, an exposure rating to enable the user to visualize a level of online exposure of their personal information, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of a set of commercial email domains determined from a scan of an email account of the user, a number of a set of protected commercial email domains, and an email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of a set of known data brokers, a number of the set of protected data brokers, and a data broker portion of the exposure rating.

BACKGROUND OF THE DISCLOSURE

1. Field of the Disclosure

Aspects of the disclosure relate generally to ensuring the privacy of a user's personal information, and more specifically, to providing an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user.

2. Description of the Related Art

There are two types of companies that may store a user's personal information: (1) “direct companies,” which are companies with which the user has a direct relationship, such as retailers from which the user has purchased a product, and (2) “data brokers” (also referred to as “data harvesters”), such as marketing companies with which the user does not have a direct relationship. With the ever-increasing use of the internet to interact with customers and/or clients, direct companies are collecting and storing more personal information about their customers and clients than ever before. Sometimes these companies may fall victim to a data breach in which sensitive personal information of their customers is leaked to hackers and scammers who can use that data to commit fraud, such as identity theft and phishing scams.

Data brokers gather, share, sell, or otherwise exploit users' personal information without the users' knowledge or consent. Spammers, scammers, telemarketers, and/or the like can purchase this user data to spam, scam, make unsolicited calls to, and/or send “junk” mail to the respective users. Additionally or alternatively, identity thieves and/or hackers can use this personal information to impersonate users and make purchases with their credit cards, take over their bank accounts, and/or the like.

While new laws have been enacted to help individuals remove these vulnerabilities, or at least to allow users to opt-out from such collection, sharing, and exposure, it is nearly impossible for individuals to take advantage of their privacy rights due to complicated opt-out processes and the sheer number of companies exposing and exploiting their personal information.

Further, for users that have begun opting-out of having their personal information exposed online, there is no way for the user to determine their current level of data exposure online. As such, a user may not take appropriate action, such as sending additional opt-out requests, applying credit freezes to their credit accounts, and the like.

SUMMARY

In an aspect, an apparatus includes one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: obtain, via the one or more transceivers, a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out; obtain, via the one or more transceivers, a number of a set of protected data brokers, wherein the set of protected data brokers comprises data brokers that are determined to not store personal information of the user, and wherein the set of protected data brokers is determined based on a scan of websites associated with a set of known data brokers for the personal information of the user; and provide, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of known data brokers, the number of the set of protected data brokers, and the data broker portion of the exposure rating.

In an aspect, an apparatus includes one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: obtain, via the one or more transceivers, a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out; obtain, via the one or more transceivers, a number of a set of protected data broker records, wherein the set of protected data broker records comprises records associated with the user that have been removed by respective data brokers of a set of known data brokers in response to opt-out requests transmitted to the set of known data brokers, wherein the set of protected data broker records is a subset of a set of data broker records associated with the user that is determined based on a scan of websites associated with the set of known data brokers for the personal information of the user; and provide, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of data broker records, the number of the set of protected data broker records, and the data broker portion of the exposure rating.

In an aspect, a method performed by a processing system includes obtaining a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out; obtaining a number of a set of protected data brokers, wherein the set of protected data brokers comprises data brokers that are determined to not store personal information of the user, and wherein the set of protected data brokers is determined based on a scan of websites associated with a set of known data brokers for the personal information of the user; and providing, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of known data brokers, the number of the set of protected data brokers, and the data broker portion of the exposure rating.

In an aspect, a method performed by a processing system includes obtaining a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out; obtaining a number of a set of protected data broker records, wherein the set of protected data broker records comprises records associated with the user that have been removed by respective data brokers of a set of known data brokers in response to opt-out requests transmitted to the set of known data brokers, wherein the set of protected data broker records is a subset of a set of data broker records associated with the user that is determined based on a scan of websites associated with the set of known data brokers for the personal information of the user; and providing, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of data broker records, the number of the set of protected data broker records, and the data broker portion of the exposure rating.

DETAILED DESCRIPTION

There are two types of companies that may store a user's personal information: (1) “direct companies,” which are companies with which the user has a direct relationship, such as retailers from which the user has purchased a product, and (2) “data brokers” (also referred to as “data harvesters”), such as marketing companies with which the user does not have a direct relationship. With the ever-increasing use of the internet to interact with customers and/or clients, direct companies are collecting and storing more personal information about their customers and clients than ever before. Sometimes these companies may fall victim to a data breach in which sensitive personal information of their customers is leaked to hackers and scammers who can use that data to commit fraud, such as identity theft and phishing scams.

Data brokers gather, share, sell, or otherwise exploit users' personal information without the users' knowledge or consent. Spammers, scammers, telemarketers, and/or the like can purchase this user data to spam, scam, make unsolicited calls to, and/or send “junk” mail to the respective users. Additionally or alternatively, identity thieves and/or hackers can use this personal information to impersonate users and make purchases with their credit cards, take over their bank accounts, and/or the like.

While new laws have been enacted to help individuals remove these vulnerabilities, or at least to allow users to opt-out from such collection, sharing, and exposure, it is nearly impossible for individuals to take advantage of their privacy rights due to complicated opt-out processes and the sheer number of companies exposing and exploiting their personal information (referred to as “first party data”).

The present disclosure provides techniques for enabling a user to opt-out of having their personal information collected and shared without their permission. More specifically, the present disclosure provides techniques for opting-out from both direct companies and data brokers.

The techniques of the present disclosure related to the personal information stored and shared by direct companies will be described with reference toFIGS.1and2. Specifically,FIG.1illustrates an example system100for implementing the opt-out techniques described herein, andFIG.2is an example signaling flow200illustrating the opt-out techniques related to direct companies described herein.

As shown inFIG.1, a plurality N of user devices110(illustrated as user devices110-1,110-2,110-3, . . .110-N) each have a privacy application115installed thereon (illustrated as privacy applications115-1,115-2,115-3, . . .115-N). A user device110may be a smartphone, a tablet computer, a laptop computer, a desktop computer, or the like. The N user devices110may optionally be in communication with a privacy server120(or a group of distributed privacy servers120). The privacy server(s)120implement an opt-out engine122and store a database of companies124. The database of companies124may be distributed across one or more physical data repositories at one or more privacy servers120or remote from the privacy server(s)120. The privacy server120is in communication with a plurality L of company interfaces130(illustrated as company interfaces130-1. . .130-L) of companies that may store users' first party data. The company interfaces130may be the companies' Internet or other network interfaces (e.g., websites, webservers, etc.).

Most of the direct companies (and organizations) with a user's first party data are likely to be found in the user's email. Thus, at stage210ofFIG.2, a privacy application115(optionally in communication with the opt-out engine122) installed on a user device110scans the user's email for companies that are likely to have the user's first party data. In some cases, this may be all the commercial email accounts/domains from which the user has ever received email. In some cases, this may be all the sender email accounts/domains that match a company in the database of companies124. In this case, the privacy application115may securely provide a list of all commercial email accounts/domains found in the user's email to the opt-out engine122, which may in turn compare that list to the database of companies124. The opt-out engine122may then return to the privacy application115a list of companies in the database of companies124that match the list of commercial email accounts/domains received from the privacy application115.

Based on the user's privacy preferences with respect to the user device110and/or email application, the user may need to grant the privacy application115permission to access the user's email application. In some cases, the user's email (or at least a portion of the user's email) may not be stored locally on the user device110, but rather, on one or more remote email servers (not shown inFIG.1or2). In those cases, the privacy application115may securely connect with the remote email server(s) to access the user's email.

Once the user's email has been scanned, at stage220, the privacy application115displays a list of the identified companies to the user via the user interface of the user device110. The privacy application115may display a select button by each entry in the list of companies and/or commercial email accounts identified at stage210to allow the user to select the corresponding company. The privacy application115may also recommend which companies to select. For example, the privacy application115may display a list of “Recommended” companies or highlight recommended companies within the list of identified companies. The recommendations may be based on information from the privacy server(s)120.

At stage230, the privacy application115selects the companies to which to send opt-out requests. The selection may be based on user input. For example, the user may select to opt-out of all identified companies, only recommended companies, only companies of a certain type (e.g., retailers, political organizations, etc.), or the like. The user may further indicate the type of opt-out desired, such as having all their first party data deleted, opting out of having their first party data shared with affiliates or other third parties, unsubscribing from promotional/marketing emails (often referred to as “spam”), requesting a copy of the data that the entity holds about the user (“right to know request”), or the like. Alternatively, the selection and type of opt-out may be selected automatically. For example, when initiating the email scan at stage210, the user may set a preference to opt-out from allowing any company to share the user's personal information.

At stage240, the privacy application115transmits opt-out requests to the selected companies (optionally via the privacy server120/opt-out engine122). Some companies handle opt-out requests by email (i.e., a user is expected to compose and send an opt-out request to the company by email) and other companies handle opt-out requests by webform (i.e., the user is expected to fill out an online form requesting the opt-out). An opt-out request is generally more effective if it comes directly from the user. Accordingly, for email-based opt-out requests, the privacy application115composes and sends emails requesting the types of opt-outs for the respective companies from the user's email account. For webform-based opt-outs, the privacy application115fills out and submits the applicable webform using any user data required by the webform (e.g., name, address, email, etc.).

At stage250, the privacy application115receives responses to the opt-out requests sent at stage240(optionally via the privacy server120/opt-out engine122). The responses may be received immediately on submission of the opt-out request (which may occur in the case of a webform request) or at some later time (e.g., after the company has manually processed the opt-out request email or webform). The responses may indicate that the opt-out was successful (e.g., the company deleted and/or will not share/sell the user's personal information), that additional information or steps are necessary, that the user's data was not found (and therefore cannot be deleted), etc. If the opt-out response is not received immediately, the response will likely be received as an email at some later time.

At stage260, based on the responses from the companies received at stage250, the privacy application115determines whether the respective opt-out requests were successful or if more information/steps are needed. The privacy application115(optionally in communication with the opt-out engine122) may use machine learning techniques to make this determination.

In greater detail, the types of opt-out procedures (e.g., email-based, webform-based) and the language of opt-out responses (e.g., successful, more steps needed, user data not found, etc.) can vary dramatically from company to company. While there are techniques to automate the task of sending opt-out requests, there is minimal, if any, automation of classifying the different types of opt-out responses. Rather, a user must manually review each opt-out response to determine if the opt-out was successful or if more information or steps are needed. Given that a single user may receive opt-out responses from hundreds, and possibly thousands, of companies, this is a significant burden to the user and a significant gap in the service provided by such an opt-out service provider. Applying machine learning to opt-out responses can allow these responses to be properly classified without user interaction, thereby dramatically improving the performance of the privacy application and the corresponding opt-out service.

In some cases, the machine learning model may be specifically trained to classify opt-out responses. For example, such a machine learning model may be trained on a large number (e.g., thousands) of full-text natural language opt-out responses that have been manually classified/labelled as particular types of opt-out responses (e.g., opt-out successful, user data successfully deleted, more user information needed, more steps needed, user data not found, etc.). That is, a training set of classified opt-out responses may be used as the input (i.e., features) to the machine learning model and the known classification types of the opt-out responses are used as the reference outputs (i.e., labels).

Alternatively, instead of using a specifically trained machine learning model, a natural language general artificial intelligence program could be used. For example, general artificial intelligence programs, such as generative pre-trained transformer (GPT) models, are being developed and commercialized. These general artificial intelligence programs have been trained to understand natural language and provide text outputs in response to their inputs. Referring to GPT models specifically, the input to a GPT model is referred to as a “prompt,” and designing a prompt is essentially how a GPT model is programmed. A prompt, therefore, usually provides instructions and/or examples of how to successfully complete the task. GPT models can be used for a large variety of tasks, such as content or code generation, summarization, conversation, and creative writing.

Thus, in this case, instead of the machine learning model being trained on a specific data set of opt-out responses, the machine learning model may be a general artificial intelligence model, such as a GPT model. The input, or prompt, to the general artificial intelligence model would be a query as to whether a given response to an opt-out request indicates that the opt-out was successful or not. The input, or prompt, may alternatively or additionally be to classify the type of the response. In this case, the input/prompt may indicate the potential classifications, such as opt-out successful, user data successfully deleted, more user information needed, more steps needed, user data not found, etc.

As another example of performing the determination at stage260, instead of a machine learning model (whether a specifically trained model or a general artificial intelligence model), a specific algorithm could be used to determine whether an opt-out was successful and optionally to classify the type of opt-out response. For example, for classifying opt-out responses, a keyword-based classification algorithm could use n-grams (e.g., 2-gram, 3-gram, 4-gram) of keywords and/or phrases that are known to be positive or negative responses, or certain types of responses, from a company. These n-grams of keywords may be stored in a database and associated with a corresponding type of opt-out response. For example, the 2-gram of the keywords “successfully processed” and the 3-gram of the keywords “completed your request” may indicate that the opt-out was successful. As another example, the 2-gram of the keywords “not found” may indicate that the user's data was not found, while the 2-gram of the keywords “more information” may indicate that more information is needed from the user.

However the type of opt-out is determined, in the case of a successful opt-out, the privacy application115may add that company to a list of successful opt-outs that may be displayed to the user on the user interface of the user device110. In the case that more information/steps are necessary to complete the opt-out, at stage270, the privacy application115may determine what the information/steps are (e.g., using machine learning and/or natural language parsing) and perform them if possible. If not, the privacy application115notifies the user that there is additional information or additional steps needed to complete the opt-out. The user may then perform those steps or provide that information manually.

More specifically, at stages260and optionally270, in the case of a successful opt-out response (e.g., user data deleted, company will not share user data, etc.), as determined at stage260, the privacy application115will mark the opt-out request as complete. In the case that more information is needed or more steps are necessary, as determined at stage260, the privacy application115may notify the user to enable the user to complete the opt-out process. If the opt-out response indicates that the user needs to fill out a form, again as determined at stage260, the privacy application115may be able to fill out the form for the user. In some cases, the determination at stage260may determine that the type of opt-out response is that the user's data was not found and therefore cannot be deleted. In that case, the privacy application115may mark the opt-out as complete and/or notify the user.

Note that a user may have multiple email accounts. As such, the procedure illustrated inFIG.2may be repeated for as many different email accounts the user wishes to process.

The techniques of the present disclosure related to the personal information stored and shared by data brokers will be described with reference toFIGS.1and3. Specifically, as described above,FIG.1illustrates an example system100for implementing the opt-out techniques described herein.FIG.3is an example signaling flow300illustrating the opt-out techniques related to data brokers described herein.

As shown inFIG.1, the privacy server(s)120may additionally store a database of data brokers126. The database of data brokers126may store information for all known data brokers, such as name, address, website, etc. Like the database of companies124, the database of data brokers126may be distributed across one or more physical data repositories at one or more privacy servers120or remote from the privacy server(s)120. The privacy server120is in communication with a plurality M of data broker interfaces140(illustrated as data broker interfaces140-1. . .140-M) of data brokers that may store/share/exploit users' first party data. The data broker interfaces140may be the companies' Internet or other network interfaces (e.g., websites, web servers, etc.).

At stage310, the opt-out engine122receives an opt-out request from a privacy application115of a user device110to have the respective user's first party data removed from any data brokers that may be storing and/or sharing that data.

At stage320, the opt-out engine122optionally performs a scan of the Internet for the user's first party data. For example, the opt-out engine122may query the data broker interfaces140of the data brokers stored in the database of data brokers126to determine if the respective data broker is storing the user's data. This stage is optional because the opt-out engine122may have previously performed this stage, for example, as part of a promotional offer, or because the opt-out engine122may send opt-out requests for the user to all data brokers stored in the database of data brokers126, regardless of whether it has been determined that a given data broker stores the user's data.

In some cases (not shown), the opt-out engine122may provide the results of the scan to the privacy application115. The results may be a total number of data brokers determined to be storing the user's first party data. The results may also list the specific data brokers found to be storing the user's data.

At stage330, the opt-out engine122transmits opt-out requests to at least the data brokers determined at stage320. Alternatively, as noted above, the opt-out engine122may send opt-out requests to all data brokers stored in the database of data brokers126. As with direct companies, some data brokers may handle opt-out requests by email (i.e., a user is expected to compose and send an opt-out request to the company by email) and other data brokers may handle opt-out requests by webform (i.e., the user is expected to fill out an online form requesting the opt-out). Accordingly, for email-based opt-out requests, the opt-out engine122composes and sends emails requesting the types of opt-outs for the respective data brokers. For webform-based opt-outs, the opt-out engine122fills out and submits the applicable webform using any user data required by the webform (e.g., name, address, email, etc. of the user).

At stage340, the opt-out engine122receives responses to the opt-out requests sent at stage330. The responses may be received immediately on submission of the opt-out request (which may occur in the case of a webform request) or at some later time (e.g., after the data broker has manually processed the opt-out request email or webform). The responses may indicate that the opt-out was successful (e.g., the data broker deleted and/or will not share/sell the user's personal information), that additional information or steps are necessary, that the user's data was not found (and therefore cannot be deleted), etc. If an opt-out response is not received immediately, the response will likely be received as an email at some later time.

At stage350, similar to stage260, based on the responses from the data brokers received at stage340, the opt-out engine122determines whether the respective opt-out requests were successful or if more information/steps are needed. As at stage260, the opt-out engine122may use machine learning techniques, rule-based techniques, or the like to make this determination.

However the type of opt-out response is determined, in the case of a successful opt-out, the opt-out engine122may add that data broker to a list of successful opt-outs that may be displayed to the user on the user interface of the user device110(not shown). In the case that more information/steps are necessary to complete the opt-out, at stage360, as at stage270, the opt-out engine122may determine what the information/steps are (e.g., using machine learning and/or natural language parsing) and perform them if possible. If not, the opt-out engine122notifies the user (not shown) that there is additional information or additional steps needed to complete the opt-out. The user may then perform those steps or provide that information manually.

As will be appreciated, the opt-out procedures illustrated inFIGS.2and3may be performed multiple times for the same user and/or email account. For example, the opt-out procedures illustrated inFIGS.2and3may be performed periodically (e.g., weekly, monthly, annually, etc.) or on-demand (e.g., upon user request/initiation).

As will also be appreciated, whileFIG.1illustrates one or more privacy servers120implementing an opt-out engine122, the techniques described herein may be performed primarily, or entirely, by the privacy application115locally on the user device110. Alternatively, the techniques described herein may be performed primarily, or entirely, by the opt-out engine122on the privacy server(s)120.FIG.2illustrates an example of a privacy application115centric approach whileFIG.3illustrates an example of an opt-out engine122centric approach. However, this is merely to serve as illustration, and the disclosure is not limited to these examples. For example, the opt-out engine122may instead perform the privacy application115operations illustrated inFIG.2, such as scanning the user's email at stage210, selecting companies at stage230, transmitting opt-out requests and receiving responses at stages240and250, determining whether the opt-outs were successful at stage260, and performing additional opt-out steps as necessary at stage270. Similarly, the privacy application115may perform the opt-out engine122operations illustrated inFIG.3, such as scanning the internet for the user's data at stage320, transmitting opt-out requests and receiving responses at stages330and340, determining whether the opt-outs were successful at stage350, and performing additional opt-out steps as necessary at stage360. In some cases, where the primary functionality described with reference toFIGS.2and3is performed by the privacy application115, the privacy application115may include the functionality of the opt-out engine122.

In some cases, the privacy application115may not be necessary at all, and the user may access the opt-out engine122via an web interface rather than the privacy application115. In this case, the opt-out engine122would perform the operations illustrated inFIG.2and interact with the user of the user device110as needed (e.g., at stages220and230) via the web interface (e.g., an online webform). Similarly, with reference toFIG.3, the opt-out engine122may receive the request to opt-out via a web interface rather than the privacy application115.

In some cases, the opt-out procedures illustrated inFIGS.2and3may be provided and performed by different vendors. For example, a first vendor may provide and perform the opt-out procedure illustrated inFIG.2and a second vendor may provide and perform the opt-out procedure illustrated inFIG.3. In this case, the first vendor may request the second vendor to perform the opt-out procedure illustrated inFIG.3for a user subscribed to the first vendor. Likewise, the second vendor may request the first vendor to perform the opt-out procedure illustrated inFIG.2for a user subscribed to the second vendor. As another alternative, a third vendor may request the first vendor and the second vendor to perform the opt-out procedures illustrated inFIGS.2and3, respectively, for a user subscribed to the third vendor. In these cases, the database of companies124and the database of data brokers126may belong to the first and second vendors, respectively. In addition, one or more privacy servers120, including an opt-out engine122, would belong to the first vendor and one or more privacy servers120, including an opt-out engine122, would belong to the second vendor. A third vendor may also have one or more privacy servers120that may include an opt-out engine122that sends requests to the first and second vendors.

For users that have begun opting-out of having their personal information exposed online (e.g., using the procedures illustrated inFIGS.2and3), it would be beneficial for those users to be provided with an indicator or rating indicating the users' current level of data exposure online. With such information, a user could take appropriate action, such as sending additional opt-out requests, applying credit freezes to their credit accounts, and the like. Additionally, tracking the increase or decrease of a user's exposure rating enables the user to easily understand how well they are doing at eliminating these data vulnerabilities. The exposure rating may also provide a critical component of various cybersecurity technologies, fraud detection, and risk algorithms. For example, third party risk technologies that assess an organization's data breach risk from third party vendors could use the exposure ratings of the users within that organization to assess and provide an organizational risk reporting and remediation. As another example, financial institutions that monitor their customers' transactions for fraud could incorporate the exposure rating as an early warning detection of likely risk of a user being targeted for fraud. As yet another example, insurance companies' risk algorithms could use the exposure rating in determining insurance rates and risks of a user. As still another example, antivirus technology and algorithms could use the exposure rating to alert users of potential cybersecurity risks. As a further example, the military and/or government could use the exposure rating in their cyber defense technology to better prevent cyberterrorism and cyberwarfare from personal data exposures.

Accordingly, the present disclosure provides techniques for determining a numerical exposure rating that indicates the extent to which a user has minimized the exposure of their personal information online. The exposure rating may thereby indicate a user's vulnerability to scams, hacking, identity theft, and spam. The exposure rating may be determined based on the results of the opt-out procedures illustrated inFIGS.2and3and may change over time (either increase or decrease) based on user actions. For example, performing only one of the opt-out procedures illustrated inFIGS.2and3may result in a base exposure rating, and performing the other opt-out procedure may increase that rating. Similarly, as opt-outs are completed for additional companies and/or data brokers, the rating may increase.

FIG.4is a diagram400illustrating an example of how a user's exposure rating may be calculated, according to one or more aspects of the disclosure. In an aspect, the privacy application115or the opt-out engine122may calculate the exposure rating. As shown inFIG.4, the exposure rating is based on a broker score and an email score. The email score may be obtained based on performance of the opt-out procedure illustrated inFIG.2and the broker score may be obtained based on performance of the opt-out procedure illustrated inFIG.3. In the example ofFIG.4, there may be two options for determining the broker score.

A first alternative for determining the broker score is to base the broker score on the number of brokers for which no record associated with the user has been found during the most recently performed opt-out procedure ofFIG.3(e.g., during the scan at stage320). Such data brokers are referred to as “protected data brokers” (or simply “protected brokers”) and include data brokers from which the user has successfully opted-out and data brokers that were never found to store records for the user. For example, an initial scan (e.g., as at stage320) may find that 50 of 92 data brokers are storing (and therefore likely exploiting) the user's personal data. As such, there are 42 protected data brokers after this initial scan, even before any opt-out requests have been sent, because there are 42 (92 minus 50) data brokers that were found to not be storing records associated with the user. Upon successfully opting-out from 34 data brokers, for example, of the initial 50 data brokers, the number of protected brokers would increase to 76 (42 plus 34). In a subsequent data broker opt-out procedure, 76 data brokers should be found to not store any personal data of the user.

In some cases, a data broker may have multiple records corresponding to the same user. For example, there may be different variations of the user's name (e.g., with or without middle name or middle initial), different variations of the user's address, and/or the like, and a data broker may have different records for each variation. Alternatively, or additionally, the data broker may have a different record for the user's name and address, name and email, name and phone number, and/or the like. In such cases, the opt-out procedure illustrated inFIG.3may need to send opt-out requests for each record associated with the user. In this case, a protected data broker is a data broker that has no records associated with the user.

Thus, in the first alternative, the privacy application115or the opt-out engine122determines the total number of protected data brokers (denoted “X”) and the number of data brokers that are still processing opt-out requests for the user (denoted “Y”). The sum of these values should equal the total number of data brokers stored in the database of data brokers126(denoted “N”). The data broker portion of the exposure rating is then calculated as (X/N)*B, where B is the portion of the exposure rating attributable to the data broker score.

In the second alternative, the privacy application115or the opt-out engine122determines the total number of protected records across all brokers (denoted “X”) and the number of records across all data brokers for which an opt-out request is still being processed (denoted “Y”). “Protected” records are user records that have been found via the opt-out procedure ofFIG.3(e.g., the scan at stage320) and successfully opted-out (e.g., removed/deleted) by the respective data broker(s) in response to opt-out requests(s) (e.g., at stage330). Protected records may also include successful verifications that the user's personal data was not found for a given data broker. The sum of these values (i.e., X and Y) should equal the total number of user records found across all data brokers (denoted “M”) stored in the database of data brokers126. The data broker portion of the exposure rating is then calculated as (X/M)*B, where B is the portion of the exposure rating attributable to the data broker score.

In some cases, instead of using the calculation described above, the broker score may be calculated as follows. The total points for the broker score (B in the above calculation) may be denoted as “tp.” The number of data brokers for which no user record was found, referred to as “not found brokers,” may be denoted as “nfb.” The number of protected records may be denoted as “pr.” The total number of records across all brokers (M in the above calculation) may be denoted as “tr.” The broker score may then be calculated as ((nfb+pr)/(nfb+tr))*tp. For example, for an initial scan where nfb=42, pr=0, tr=50, and tp=275, the broker score may be calculated as ((42+0)/(42+50)*275=125.54.

Referring now to the email score, the privacy application115or the opt-out engine122determines the total number of protected email domains (denoted “X”) and the number of email domains for which an opt-out request is still being processed (denoted “Y”). “Protected” email domains are commercial email domains that were found via the opt-out procedure ofFIG.2(e.g., during the scan at stage210) and from which the user has successfully opted-out. The sum of these values (i.e., X and Y) should equal the total number of commercial email domains (denoted “L”) found in the user's email account (e.g., as at stage210) or selected for opt-out (e.g., as at stage230). The email portion of the exposure rating is then calculated as (X/L)*E, where E is the portion of the exposure rating attributable to the email score.

In some cases, instead of using the calculation described above, the email score may be calculated as follows. The total number of vulnerabilities (L in the above calculation) may be denoted as “ev.” This value may have an upper bound, such that the a user may have a value of ev that is less than or equal to this upper bound. The total number of vulnerabilities, regardless of an upper bound on ev, may be denoted as “tv.” The variable tv may be greater than or equal to ev. That is, where tv is less than or equal to the upper bound on ev, tv may be equal to ev. However, where tv is greater than the upper bound on ev, tv will be greater than ev. The upper bound on ev, which is a maximum number of vulnerabilities, may be denoted as “mv.” The number of secured vulnerabilities (X in the above calculation) may be denoted as “sv.” The total points for the email score (E in the above calculation) may be denoted as “maxScore.” The email score may then be calculated as ((1−ev/my)+((ev/mv)(sv/tv)))*maxScore. For example, for a scan where ev=50, mv=750, sv=10, tv=50, and maxScore=275, the email score may be calculated as ((1−50/750)+((50/750)(10/50)))*275=260.33.

Referring back to the first email score calculation, in some cases, the value of L may have an upper limit, regardless of the number of commercial domains found/selected for opt-out. This upper limit may be determined based on crowd sourced data from some set of users. For example, based on the crowd sourced data, a maximum number of email exposures/vulnerabilities (e.g., commercial email domains from which a user wishes to opt-out) may be determined across the email accounts of all of the users. That is, based on email scans of email accounts of the set of users and their selections of email domains from which to opt-out, a maximum number of commercial domains that represent data exposures may be determined. This maximum may then be used as the upper limit, or the upper limit may be based on that average (e.g., the upper limit may be 100 email domains more than the maximum).

Similarly, an average number of data exposures/vulnerabilities may be determined across the email accounts of all of the users. The email score of a given user can then be compared to the average of all users. In this case, a number of email exposures less than the average may result in a higher email score, whereas a number of email exposures greater than the average may result in a lower email score. For example, a particular user's email score may be weighted based on whether the number of email exposures is more or less than the average.

In some cases, the exposure rating may have a lower bound (e.g., a minimum rating) and an upper bound (e.g., a maximum rating). For example, the maximum exposure rating may be 850 and the minimum exposure rating may be 300. A user's exposure rating may therefore fall within a range between the lower bound (e.g., 300) and the upper bound (e.g., 850), and the broker score may account for half that range (e.g., half of 550, or 275) and the email score may account for the other half of that range.

The following are examples of determining the broker score and email score based on an exposure rating that may be between 300 and 850 points. As a specific example of the first alternative for determining the broker score, it is assumed that there are 92 data brokers (N), and records associated with the user were found at 50 data brokers. In this case, as in the example above, there are currently 42 protected data brokers (X). If the data broker score accounts for half the user's exposure rating (here, half of 550 points), then the portion of the exposure rating attributable to the data broker score is 275 points (B=275). The broker score would therefore be 42 (X) divided by 92 (N) times 275, which equals 125.5 points.

As a specific example of the second alternative for determining the broker score, it is assumed that there are a total of 150 records for the user (M) across the 92 data brokers, and 120 records have been protected (i.e., were successfully opted-out) (X). As in the previous example, if the data broker score accounts for half the user's exposure rating (here, half of 550 points), then the portion of the exposure rating attributable to the data broker score is 275 points (B=275). The broker score would therefore be 120 (X) divided by 150 (M) times 275, which equals 220 points.

As a specific example of determining the email score, it is assumed that there are a total of 150 commercial email domains from which the user wishes to opt-out (L), and 120 email domains have been protected (i.e., were successfully opted-out) (X). If the email score accounts for half the user's exposure rating (here, half of 550 points), then the portion of the exposure rating attributable to the email score is 275 points (E=275). The email score would therefore be 120 (X) divided by 150 (L) times 275, which equals 220 points.

Thus, with reference to the foregoing examples, where the first alternative is used to determine the data broker score, the user's exposure rating would be 300 (the minimum exposure rating) plus 125.5 (the data broker score) plus 220 (the email score) for a total exposure rating of 675.5. Where the second alternative is used to determine the data broker score, the user's exposure rating would be 300 (the minimum exposure rating) plus 220 (the data broker score) plus 220 (the email score) for a total exposure rating of 740.

In some cases, in addition to a numeric exposure rating, the privacy application115or the opt-out engine122may provide a textual exposure rating, such as POOR, FAIR, GOOD, or EXCELLENT. The textual exposure rating may be based on the numerical exposure rating. For example, a numerical exposure rating of 300 points may correspond to a textual exposure rating of POOR, a numerical exposure rating around 500 points may correspond to a textual exposure rating of FAIR, a numerical exposure rating around 600 points may correspond to a textual exposure rating of GOOD, and a numerical exposure rating around 800 points may correspond to a textual exposure rating of EXCELLENT.

For example, when a new user signs up for the exposure rating, the user's initial exposure rating may be set to 300 points (POOR). When the user performs a broker scan (e.g., as at stage320), the exposure rating may increase to a value around 500 (FAIR). When the user enrolls and starts successfully opting-out from the data brokers, the user's exposure rating may increase to around 600 (GOOD). When the user performs an email scan (FIG.2), the user's exposure rating may increase to around 800 (EXCELLENT). Thus, opting-out of personal data exposures and removing unnecessary information from both direct companies and data brokers elevates a user's score, signifying improved privacy and security.

As noted above, different vendors may provide/perform the different opt-out procedures illustrated inFIGS.2and3. In some cases, yet another vendor may determine a user's exposure rating. In this case, the vendor providing the exposure rating may request the email opt-out procedure (FIG.2) and the data broker opt-out procedure (FIG.3) from the respective vendor(s). Alternatively, the vendor providing/performing the email opt-out procedure (FIG.2) and/or the data broker opt-out procedure (FIG.3) may also determine the exposure score.FIG.5is a diagram500illustrating different scenarios regarding which vendor(s) may provide the email opt-out procedure, the data broker opt-out procedure, and the exposure rating, according to aspects of the disclosure.

While the foregoing has described the exposure rating as being based primarily on a broker score and an email score, as will be appreciated, the exposure rating may be based on other factors as well. For example, the exposure rating may consider whether a user's personal information has been exposed on the Dark Web, exposed on one or more social media platforms, whether the user is already a victim of identity theft or other data harvesting, and/or the like. The exposure rating may additionally or alternatively be based on information gathered from the user that indicates other ways that the user's personal information might be exposed, such as financial transactions or purchases, types and numbers of devices the user uses, and/or the like. In these scenarios, rather than half the exposure rating being based on the broker score and half the exposure rating being based on the email score, different percentages of the exposure rating may be allocated to the different types of exposure (e.g., data broker exposure, email exposure, Dark Web exposure, social media exposure, etc.). In addition, the specific percentages allocated to the different types of exposures may change over time as new types of exposures are added or existing ones are removed or the weightings assigned to the exposure types change.

Further, as briefly noted above, an exposure rating can be assigned to an organization. In this case, a set of users in the organization (e.g., all members of the organization, all management level employees of the organization, the Board of Directors of the organization, etc.) may obtain individual exposure ratings (e.g., by performing the opt-out procedures illustrated inFIGS.2and3). The individual exposure ratings may then be aggregated together into a single exposure rating for the organization. In some cases, the individual exposure ratings may be weighted in the aggregated exposure rating based on the respective users' position in the organization. For example, a manager's exposure rating may have a higher weighting than an intern's exposure rating.

FIG.6is a block diagram illustrating various components of an example user device600, according to aspects of the disclosure. In an aspect, the user device may correspond to any of the user devices described herein, such as user device110inFIG.1. As a specific example, the user device600may be a smartphone, a tablet computer, a laptop computer, a desktop computer, or the like.

For the sake of simplicity, the various features and functions illustrated in the block diagram ofFIG.6are connected together using a common data bus that is meant to represent that these various features and functions are operatively coupled together. Those skilled in the art will recognize that other connections, mechanisms, features, functions, or the like, may be provided and adapted as necessary to operatively couple and configure an actual user device. Further, it is also recognized that one or more of the features or functions illustrated in the example ofFIG.6may be further subdivided, or two or more of the features or functions illustrated inFIG.6may be combined.

The user device600may include one or more transceivers604connected to one or more antennas602and providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) with other network nodes, such as other user devices110and/or the privacy server(s)120via at least one designated radio access technology (RAT) (e.g., Wi-Fi, Long-Term Evolution (LTE), Fifth Generation New Radio (5G NR), etc.) over one or more wireless communication links. The one or more transceivers604may be variously configured for transmitting and encoding wireless signals (e.g., messages, indications, information, and so on), and, conversely, for receiving and decoding wireless signals (e.g., messages, indications, information, pilots, and so on) in accordance with the designated RAT. In an aspect, the one or more transceivers604and the antenna(s)602may form a (wireless) communication interface of the user device600.

As used herein, a “transceiver” may include at least one transmitter and at least one receiver in an integrated device (e.g., embodied as a transmitter circuit and a receiver circuit of a single communication device) in some implementations, may comprise a separate transmitter device and a separate receiver device in some implementations, or may be embodied in other ways in other implementations. In an aspect, a transmitter may include or be coupled to a plurality of antennas (e.g., antenna(s)602), such as an antenna array. Similarly, a receiver may include or be coupled to a plurality of antennas (e.g., antenna(s)602), such as an antenna array. In an aspect, the transmitter(s) and receiver(s) may share the same plurality of antennas (e.g., antenna(s)602), such that the user device600can only receive or transmit at a given time, not both at the same time. In some cases, a transceiver may not provide both transmit and receive functionalities. For example, a low functionality receiver circuit may be employed in some designs to reduce costs when providing full communication is not necessary (e.g., a receiver chip or similar circuitry simply providing low-level sniffing).

The user device600may also include a satellite positioning system (SPS) receiver606. The SPS receiver606may be connected to the one or more SPS antennas603and may provide means for receiving and/or measuring satellite signals. The SPS receiver606may comprise any suitable hardware and/or software for receiving and processing SPS signals, such as global positioning system (GPS) signals. The SPS receiver606requests information and operations as appropriate from the other systems, and performs the calculations necessary to determine the UE's600position using measurements obtained by any suitable SPS algorithm.

One or more sensors608may be coupled to one or more processors610and may provide means for sensing or detecting information related to the state and/or environment of the user device600, such as speed, heading (e.g., compass heading), headlight status, gas mileage, etc. By way of example, the one or more sensors608may include a speedometer, a tachometer, an accelerometer (e.g., a microelectromechanical systems (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric pressure altimeter), etc.

The one or more processors610may include one or more central processing units (CPUs), microprocessors, microcontrollers, ASICs, processing cores, DSPs, field-programmable gate arrays (FPGAs), or the like that provide processing functions, as well as other calculation and control functionality. The one or more processors610may therefore provide means for processing, such as means for determining, means for calculating, means for receiving, means for transmitting, means for indicating, etc. The one or more processors610may include any form of logic suitable for performing, or causing the components of the user device600to perform, at least the techniques described herein.

The one or more processors610may also be coupled to a memory614providing means for storing (including means for retrieving, means for maintaining, etc.) data and software instructions for executing programmed functionality within the user device600. The memory614may be on-board the one or more processors610(e.g., within the same integrated circuit (IC) package), and/or the memory614may be external to the one or more processors610and functionally coupled over a data bus.

The user device600may include a user interface650that provides any suitable interface systems, such as a microphone/speaker652, keypad654, and display656that allow user interaction with the user device600. The microphone/speaker652may provide for voice communication services with the user device600. The keypad654may comprise any suitable buttons for user input to the user device600. The display656may comprise any suitable display, such as, for example, a backlit liquid crystal display (LCD), and may further include a touch screen display for additional user input modes. The user interface650may therefore be a means for providing indications (e.g., audible and/or visual indications) to a user and/or for receiving user input (e.g., via user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on).

In an aspect, the user device600may include a privacy application670(which may correspond to privacy application115) coupled to the one or more processors610. The privacy application670may be a hardware, software, or firmware component that, when executed, causes the user device600to perform the operations described herein. For example, the privacy application670may be a software module stored in memory614and executable by the one or more processors610. As another example, the privacy application670may be a hardware circuit (e.g., an ASIC, a field-programmable gate array (FPGA), etc.) within the user device600.

FIG.7illustrates several example components (represented by corresponding blocks) that may be incorporated into a privacy server700(which may correspond to a privacy server120) to support the operations described herein.

The privacy server700may include one or more network transceivers790providing means for communicating (e.g., means for transmitting, means for receiving, etc.) with other network entities (e.g., other privacy servers700/120). For example, the privacy server700may employ the one or more network transceivers790to communicate with other privacy servers700/120over one or more wired or wireless network interfaces.

The privacy server700may also include other components that may be used in conjunction with the operations as disclosed herein. The privacy server700may include one or more processors794for providing functionality relating to, for example, opt-out procedures, as described herein, and for providing other processing functionality. The one or more processors794may therefore provide means for processing, such as means for determining, means for calculating, means for receiving, means for transmitting, means for indicating, etc. In an aspect, the one or more processors794may include, for example, one or more general purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), other programmable logic devices or processing circuitry, or various combinations thereof.

The privacy server700may include memory circuitry implementing one or more memories796(e.g., each including a memory device) for maintaining information (e.g., the database of companies124, the database of data brokers126, and so on). The one or more memories796may therefore provide means for storing, means for retrieving, means for maintaining, etc. In some cases, the privacy server700may include an opt-out engine798(which may correspond to opt-out engine122). The opt-out engine798may be hardware circuits that are part of or coupled to the one or more processors794that, when executed, cause the privacy server700to perform the functionality described herein. In other aspects, the opt-out engine798may be external to the one or more processors794(e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, the opt-out engine798may be a memory module stored in the one or more memories796that, when executed by the one or more processors794(or a modem processing system, another processing system, etc.), cause the privacy server700to perform the functionality described herein.FIG.7illustrates possible locations of the opt-out engine798, which may be, for example, part of the one or more network transceivers790, the one or more memories796, the one or more processors794, or any combination thereof, or may be a standalone component.

The various components of the privacy server700may be communicatively coupled to each other over a data bus792. In an aspect, the data bus792may form, or be part of, a communication interface of the privacy server700. For example, where different logical entities are embodied in the same device, the data bus792may provide communication between them.

The components ofFIG.7may be implemented in various ways. In some implementations, the components ofFIG.7may be implemented in one or more circuits such as, for example, one or more processors and/or one or more ASICs (which may include one or more processors). Here, each circuit may use and/or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks790to798may be implemented by processor and memory component(s) of the privacy server700(e.g., by execution of appropriate code and/or by appropriate configuration of processor components). For simplicity, various operations, acts, and/or functions are described herein as being performed “by a privacy server.” However, as will be appreciated, such operations, acts, and/or functions may actually be performed by specific components or combinations of components of the privacy server700, such as the one or more processors794, the one or more network transceivers790, the one or more memories796, the opt-out engine798, etc.

FIG.8illustrates an example method800, according to one or more aspects of the disclosure. In an aspect, the method800may be performed by a processing system. The processing system may be a server (e.g., privacy server120, privacy server700) or a user device (e.g., user device110, user device600). Alternatively, the processing system may be the processing system of a server (e.g., the one or more processors794, the opt-out engine798, etc.) or a user device (e.g., the one or more processors610, the privacy application670, etc.).

At810, the processing system obtains, via one or more transceivers (e.g., transceiver(s)604, network transceiver(s)790), a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out.

In an aspect, where the processing system is a user device or processing system of the user device, operation810may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation810may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

At820, the processing system obtains, via the one or more transceivers, a number of a set of protected data brokers, wherein the set of protected data brokers comprises data brokers that are determined to not store personal information of the user, and wherein the set of protected data brokers is determined based on a scan of websites associated with a set of known data brokers for the personal information of the user; and

In an aspect, where the processing system is a user device or processing system of the user device, operation820may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation820may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

At830, the processing system provides, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of known data brokers, the number of the set of protected data brokers, and the data broker portion of the exposure rating.

In an aspect, where the processing system is a user device or processing system of the user device, operation830may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation830may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

In some cases, where the processing system is or is included in a user device of the user, providing the exposure rating at operation830may include displaying the exposure rating on a user interface of the user device.

In some cases, where the processing system is a server or is included in a server in communication with a user device of the user, providing the exposure rating at operation830may include transmitting, via one or more transceivers, the exposure rating to the user device.

In some cases, the method800may further include (not shown) receiving, via one or more transceivers, a request from the user to opt-out the email account of the user from the set of commercial email domains identified in the email account of the user.

In some cases, the method800may further include (not shown) transmitting, via one or more transceivers, in response to the request from the user, a request to a second apparatus (e.g., another server, such as one belonging to Vendor1in Scenario1ofFIG.5) to opt-out the email account of the user from the set of commercial email domains identified in the email account of the user. In this case, obtaining the number of the set of protected commercial email domains at operation810may include receiving, via the one or more transceivers, the number of the set of protected commercial email domains from the second apparatus. Further, the method800may further include receiving, via the one or more transceivers, the number of the set of commercial email domains from the second apparatus.

In some cases, obtaining the number of the set of protected commercial email domains at operation810may include transmitting, via one or more transceivers, in response to the request from the user, opt-out requests for the email account of the user to commercial entities corresponding to the set of commercial email domains, receiving, via the one or more transceivers, the successful opt-out responses from commercial entities corresponding to the set of protected commercial email domains, and determining the number of the set of protected commercial email domains based on reception of the successful opt-out responses from the set of protected commercial email domains.

In some cases, the method800may further include (not shown) receiving, via one or more transceivers, a request from the user to opt-out the personal information of the user from the set of known data brokers.

In some cases, the method800may further include (not shown) transmitting, via one or more transceivers, in response to the request from the user, a request to a second apparatus (e.g., another server, such as one belonging to Vendor2in Scenario1ofFIG.5) to opt-out the personal information of the user from the set of known data brokers. In this case, obtaining the number of the set of protected data brokers at operation820may include receiving, via the one or more transceivers, the number of the set of protected data brokers from the second apparatus. Further, the method800may further include receiving, via the one or more transceivers, the number of the set of data brokers from the second apparatus.

In some cases, obtaining the number of the set of protected data brokers at operation820may include performing, via one or more transceivers, the scan of the websites associated with the set of known data brokers for the personal information of the user, and determining, based on the scan, the set of protected data brokers. In this case, the method800may further include (not shown) transmitting, via one or more transceivers, in response to the request from the user, opt-out requests for the personal information of the user to a first subset of the set of known data brokers other than the set of protected data brokers, wherein the first subset of the set of known data brokers is determined to store the personal information of the user based on the scan. The method800may further include (not shown) receiving, via the one or more transceivers, successful opt-out responses from a second subset of the set of known data brokers, wherein the second subset of the set of known data brokers is a subset of or corresponds to the first subset of the set of known data brokers. In some cases, the set of protected data brokers includes the second subset of the set of known data brokers based on the scan being performed after reception of the successful opt-out responses, or the set of protected data brokers does not include the second subset of the set of known data brokers based on the opt-out requests being transmitted after the scan.

In some cases, the exposure rating may greater than or equal to a minimum value and less than or equal to a maximum value, the email portion of the exposure rating may be a first portion of a difference between the minimum value and the maximum value, and the broker portion of the exposure rating may be a second portion of the difference between the minimum value and the maximum value. In some cases, the first portion may be half the difference between the minimum value and the maximum value, and the second portion may be half the difference between the minimum value and the maximum value.

In some cases, the email score may be determined as the number of the set of protected commercial email domains divided by the number of the set of commercial email domains multiplied by the email portion of the exposure rating, and the data broker score may be determined as the number of the set of protected data brokers divided by the number of the set of known data brokers multiplied by the data broker portion of the exposure rating.

In some cases, the exposure rating may be determined as a sum of the email score and the data broker score.

FIG.9illustrates an example method900, according to one or more aspects of the disclosure. In an aspect, the method900may be performed by a processing system. The processing system may be a server (e.g., privacy server120, privacy server700) or a user device (e.g., user device110, user device600). Alternatively, the processing system may be the processing system of a server (e.g., the one or more processors794, the opt-out engine798, etc.) or a user device (e.g., the one or more processors610, the privacy application670, etc.).

At910, the processing system obtains, via one or more transceivers (e.g., transceiver(s)604, network transceiver(s)790), a number of a set of protected commercial email domains, wherein the set of protected commercial email domains comprises commercial email domains to which opt-out requests for an email account of a user have been transmitted and from which successful opt-out responses have been received, and wherein the set of protected commercial email domains is a subset of a set of commercial email domains identified in the email account of the user and selected for opt-out.

In an aspect, where the processing system is a user device or processing system of the user device, operation910may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation910may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

At920, the processing system obtains, via the one or more transceivers, a number of a set of protected data broker records, wherein the set of protected data broker records comprises records associated with the user that have been removed by respective data brokers of a set of known data brokers in response to opt-out requests transmitted to the set of known data brokers, wherein the set of protected data broker records is a subset of a set of data broker records associated with the user that is determined based on a scan of websites associated with the set of known data brokers for the personal information of the user; and

In an aspect, where the processing system is a user device or processing system of the user device, operation920may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation920may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

At930, the processing system provides, to the user, an exposure rating for the user to enable the user to visualize a level of online exposure of the personal information of the user, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of the set of commercial email domains, the number of the set of protected commercial email domains, and the email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of the set of data broker records, the number of the set of protected data broker records, and the data broker portion of the exposure rating.

In an aspect, where the processing system is a user device or processing system of the user device, operation930may be performed by the one or more transceivers604, the one or more processors610, memory614, and/or the privacy application670, any or all of which may be considered means for performing this operation. Where the processing system is a server or a processing system of the server, operation930may be performed by the one or more network transceivers790, the one or more processors794, memory796, and/or the opt-out engine798, any or all of which may be considered means for performing this operation.

While the foregoing disclosure shows illustrative aspects of the disclosure, it should be noted that various changes and modifications could be made herein without departing from the scope of the disclosure as defined by the appended claims. For example, the functions, steps and/or actions of the method claims in accordance with the aspects of the disclosure described herein need not be performed in any particular order. Further, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly described as such. Furthermore, as used herein, the terms “set,” “group,” and the like are intended to include one or more items and may be used interchangeably with “at least one,” “one or more,” and the like. Also, as used herein, the terms “has,” “have,” “having,” and the like are intended to be open-ended terms that do not limit an element that they modify (e.g., an element “having” A may also have B). Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and/or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of”) or the alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more”). Furthermore, although components, functions, actions, and instructions may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. Accordingly, as used herein, the articles “a,” “an,” “the,” and “said” are intended to include one or more items and may be used interchangeably with “at least one,” “one or more,” and the like. Additionally, as used herein, the terms “at least one” and “one or more” encompass “one” component, function, action, or instruction performing or capable of performing a described or claimed functionality and also “two or more” components, functions, actions, or instructions performing or capable of performing a described or claimed functionality in combination.