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    • 专利摘要:
    Does the assessment of a consumer harm risk related to a data breach include determining, for the particular data breach, a data breach score, referred to as a Breach Clarity score? (BC), indicative of the risk of damage related to the particular violation. A data structure matches a breached information element with at least one potential damage. The algorithms assign an injury risk score to the damage, determine an element risk score for the information damage-element pair, and determine a BC score, using the risk of damage and the risk scores of the element, and a exposure score. The BC score can be modified by a scheduling algorithm to generate a relative BC score. The system identifies and ranks the actions for mitigating orders for the violation and transmits those with the BC score to the consumer. One of the demographic and / or behavioral characteristics of the consumer can be taken into account in the exposure score and in the ranking of mitigation actions.
    公开号:BR112020003492A2
    申请号:R112020003492-4
    申请日:2018-08-21
    公开日:2020-08-25
    发明作者:James Van Dyke
    申请人:Breach Clarity, Inc.；
    IPC主号:
    专利说明:

    [001] [001] This application claims priority for and benefit from United States Provisional Patent Application 62 / 548,656, filed on August 22, 2017, which is hereby incorporated by reference in its entirety. Technical Field
    [002] [002] The present description refers to a method and system for determining a consumer harm risk that includes identifying theft resulting from a data breach or data compromise. Foundations
    [003] [003] Data breaches and data compromises are very different from each other in terms of both the total relative risk and the specific nature of this risk for a consumer victim of the breach and, as a result, requires that priority action steps and exclusive are taken by a victim consumer in response to notification of a breach or compromise of the victim victim's data. Currently, data breach victims only have access to overly general fraud protection advice or solutions, which may include inappropriate advice or solutions. The advice available to a data breach victim can be distracting in that it may not be possible even for the most qualified individual human counselor to compute recommendations that precisely explore, for example, include and reflect, the expertise of a wide range of fraud prevention and identity protection specialists. Summary of the Invention
    [004] [004] A method and system for assessing a data breach and providing recommendations for mitigation actions to reduce the risk to the consumer of identity theft or other damage following the
    [005] [005] Another computer generated output of the BC system described here is a prioritized list of damages in particular (such as tax refund fraud or existing credit card fraud) that are generated by an algorithm as the most possible, for example , most likely, damages that may occur as a result of a particular breach event or a combination of breach events, based on the unique characteristics of that particular breach or this combination of breach events in particular. Another way out of the BC system is an element risk score for damage associated with a breached information element, where the element risk score is generated using one or more algorithms applied to the associated data in a data structure and / or an industry survey including qualitative, quantitative and non-quantitative research, and stored in a BC system data structure. The element's risk scores for the information elements breached in a data breach event can be combined using an algorithm to derive the overall BC score for a breach event. Yet another output generated by the BC system using the data structure is a prioritized list of consumer fraud mitigation action steps in particular, which can include, for example, actions such as getting a credit freeze, setting an alert fraud, start credit monitoring, etc. which are rated to generate a defined priority action to identify the relatively strongest protective actions against the identified risks and damages. The outputs generated by the BC system are presented, for
    [006] [006] The BC system described here includes an apparatus, comprising a computing device that has a processor and a non-transitory memory, the non-transitory memory storing instructions executable by the processor in such a way that the apparatus is configured and / or operable to perform a method described herein that can also be referred to as a Breach Clarity ™ (BC) process, or BC method. In an illustrative example, the method may include filling, through a server, a data structure with the breach information, wherein the breach information may include a plurality of information elements and a plurality of damages. Each information element of the plurality of information elements is paired in the data structure with each damage of the plurality of damage to generate a plurality of information element-damage data pairs. The method includes generating, using an algorithm, an element risk score for each respective element-information damage pair from the plurality of element-information damage data pairs, and associating, in the data structure, the risk score of the element with the respective data element-damage data pair.
    [007] [007] Data breach information can include a breach event descriptor that identifies a breach event, and at least one breached information element, where at least one breach element
    [008] [008] In one example, the method includes generating, using the algorithm, a damage risk score for the respective damage of each data element-damage data pair associated with the violation event descriptor, associate, using the data structure, the damage risk score for each damage with the breach event descriptor, and store in the data structure the damage risk score associated with the breach event descriptor. The method may include generating, using the algorithm, a data breach score for the breach event, in which the generation of the data breach score includes adding the respective damage risk scores for each element data pair. -information damage associated with the breach event descriptor to generate the data breach score. In one example, the data breach score is calculated by the algorithm with an absolute value. In another example, the data breach score is calculated by the algorithm with a relative value, where the relative value can be generated using the algorithm, by applying at least one of a scaling factor and a modifier in the breach score of data. The method may include generating, using the algorithm, an exposure score for the breach event, and associating, in the data structure, the exposure score with the breach event descriptor.
    [009] [009] The method may include transmitting, through the server, the data breach score to a user interface, where the user interface can stay in communication with the server. In one example, the
    [0010] [0010] The features and advantages noted earlier and still others of the present description are readily apparent from the following detailed description when taken in connection with the attached drawings. Brief Description of Drawings
    [0011] [0011] Figure 1 is a schematic illustration of an exemplary Breach Clarity ™ (BC) system to generate outputs related to the risk related to a violation event; figure 2 is a schematic illustration of a flowchart of an exemplary process to generate the outputs related to the risk related to
    [0012] [0012] A method and system for assessing a data breach and providing recommendations for mitigation actions to reduce the risk to the consumer of identity theft or other damage, following awareness and / or notification that the consumer has been exposed to risk due to a breach or compromise of data in particular of one or more of the consumer information elements, are described here. In an illustrative, non-limiting example, elements of information that may be breached and / or compromised may include one or more of personally identifiable information (PII), protected health information (PHI), payment card industry data ( PCI), and other such information that may, if violated and / or compromised, expose the violated victim to risk, injury and / or damage. A consumer who has been the victim of a data breach may be referred to here as a consumer, as a victim consumer and / or as a victim. The term “data breach”, as used herein, should not be limiting, and should be interpreted broadly to understand any incident in which the data has been exposed in a way that creates a possibility or potential for damage, impairment, loss and / or injury to the data subject, including, for example, identity theft, financial loss, loss of privacy, etc. A “data breach”, as the term is used here, can also be referred to and / or comprise one or more of the theft of data, compromise of data, unauthorized access to data, unauthorized exposure of data, a data hack, data intrusion, data penetration, etc. A “data breach” can also be referred to here as a “data breach”, a “data breach event” and / or as a “breach event”.
    [0013] [0013] In relation to the drawings in which equal reference numbers represent equal components for all the different figures, the elements
    [0014] [0014] In relation to figure 1, a system, which can be described here
    [0015] [0015] As shown in figure 1, the BC 12 server includes a memory 16 and a central processing unit (CPU) 14. The memory 16 of the BC 12 server can include, as an example, Exclusive Read Memory (ROM) ), Random Access Memory (RAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc., that is, non-transient / tangible machine memory of a size and speed sufficient to store data structure 22, algorithms 10, the tab formats included in data structure 22, such as data tables 22A, 22B, 22C, 22D shown in figures 3, 4, 6 and 11, respectively, quantitative, qualitative and other industry surveys and / or related to the breach, breach event data, mitigation action information, one or more BC 20 applications, etc. Memory 16 is of sufficient size and speed to manipulate data structure 22, to execute algorithms 10 and / or BC 20 applications to generate risk-related outputs, and to generate one or more user interfaces (UI) 90, including, for example, the 90A-90E user interfaces shown in the figures. The BC server includes a BC 18 interface, which, in an illustrative example, can be configured as a modem, browser, or similar device suitable for accessing a network 130. In one example, network 130 is the Internet. The BC 12 server, in a non-limiting example, is administered and / or operated by a BC service provider. In one example, a victim consumer can access risk exits and other BC 100 system services through a user device 30 and / or by personal contact with the BC service provider.
    [0016] [0016] A consumer, also referred to here as a consumer, can access the BC 100 system, for example, through a user device 30, to view the breach information that includes the risk outputs generated by the BC 100 system for one or more breach events 70. In one example, the consumer accessing the BC 100 system may be a consumer victim of a breach 70 event accessing the BC 100 system
    [0017] [0017] In a non-limiting example, the subscriber-consumer subscription information and / or the consumer profile of the subscriber-consumer
    [0018] [0018] User device 30 includes a memory 26, a central processing unit (CPU) 28, one or more user applications 24, a communications interface 126, and an input / output interface 128. The user device 30 can be a user device, such as a cell phone, a personal digital assistant (PDAs), a handheld or portable device (iPhone, Blackberry, etc.), a notebook, a personal computer, a notepad or other user configured for mobile communications, including communication with network 130. User device 30 is configured to communicate with network 130 through communications interface interface 126, which may be a modem, a mobile browser, wireless internet browser or similar device suitable for accessing the network 130. The memory 26 of the user device 30 may include, for example, Exclusive Reading Memory (ROM), Random Access Memory (RAM), Exclusive Reading Memory Electrically Erasable Programmable (EEPROM), etc., that is, a non-transient / tangible machine memory of sufficient size and speed to run a BC 20 application that can be activated on user device 30, including, for example, a or more user interfaces 90 and / or to perform mitigation actions 116, as described herein with further details. The input / output interface 128 of user device 30 may include, for example, one or more of a numeric keypad and display, a touchscreen, or a combination of them configurable to transmit and / or display, for example, one or more user interfaces 90 associated with one or more BC 20 applications and / or to display the content received by user device 30 from the
    [0019] [0019] System 100 may include one or more reporting servers 40 configured and / or operable to report information related to a data breach, which may include, for example, a breach descriptor 70 of the breached entity, such as a company name (for example, “Jewelery Azure” or “Banco XYZ”), information on the breach event that includes breached date (s), information elements 68 breached and / or compromised by the breach (personally identifiable information (PII), protected health information (PHI), payment card industry data (PCI), etc.), information regarding the violating entity (hacker, criminal, etc.), exposure and / or post-violation use breached data (availability for sale in online criminal markets), etc. Each of the reporting servers 40 is administered and / or operated by a reporting entity that is reporting a breach event. The reporting entity that manages a reporting server 40 can be, for example, a breached entity that reports information related to a breach of its own data, a regulatory or government organization set up to receive the information from the breached entities and / or to report information to consumer victims, a financial institution, a government organization, a health organization, a
    [0020] [0020] Reporting server 40 includes a reporting interface 38, which, in an illustrative example, can be configured as a modem, a
    [0021] [0021] System 100 may include one or more resource servers 50 configured to provide resources, including mitigation actions 116 (see figure 8), for consumers who are victims of a data breach. Each of the resource servers 50 is administered and / or operated by a resource provider. A resource provider may be, by way of non-limiting example, a financial institution, such as a bank or broker, which provides a notification service for a consumer victim subject to a breach of the financial institution's customer information, a credit bureau or similar organization that monitors the victim-victim's account for fraud and / or identity theft detection, an identity protection software provider and / or the breached entity, for example, to change a password or other breached information, such as a payment card account number, etc. In an illustrative example, the resource server 50 includes a memory 42 and a central processing unit (CPU) 44. The memory 42 of the resource server 50 may include, by way of example, Unique Read Memory (ROM), Memory Random Access (RAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc., that is, non-transient / tangible machine memory of a size and speed sufficient to provide resource services, which may include mitigation actions 16, related to the breach event and / or the breached information, which can be stored and / or accessed through a resource database 46. The resource server 50 includes a resource interface 48 which, in an illustrative example, can be configured as a modem, a browser, an Internet page or a similar device suitable for accessing a network
    [0022] [0022] In another illustrative example, resource server 50 may incorporate a third party source of breach status information or other details, such as IDtheftcenter.org, a credit reporting agency, an activity monitoring system for online monitoring of activity related to one of the online consumer accounts, email addresses, etc., such as www.haveIbeenpwnd.com, a website of the violated entity itself established for a consumer to obtain information from violation from this violated entity, such as the https://trustedidpremier.com/eligibility/eiligibility.html website established for the victims of the Equifax violation event. In this example, resource server 50 can be integrated with server BC 12, via an API or similar system, in such a way that resource server 50 can automatically provide violation information to the BC server
    [0023] [0023] In the example shown in figure 1, the BC server 12, user device 30, reporting server 40, and resource server 50 can selectively communicate with each other over network 130. The example shown in Figure 1 is non-limiting, in such a way that one or more of the BC server 12, the user device 30, the reporting server 40, the resource server 50 can be selectively connected directly, for example, to directly access each other and / or for off-network data communication between one or more of the BC server 12, user device 30, reporting server 40, resource server 50. The example
    [0024] [0024] In relation to figures 1 and 2, figure 2 is a 200A flow chart that illustrates a high level overview of the Breach Clarity ™ process, including both consumer input and output as supports, with an abbreviated overview of the BC 10 algorithm in the middle. The Breach Clarity ™ process includes a method 200 described herein, which, by way of non-limiting example, comprises flowcharts 200A, 200B and 200C. As shown in figure 2, in step 52, a consumer-victim, also referred to here as a consumer, inserts the name of the violation event 70 in
    [0025] [0025] In step 58, an algorithm 10 is applied to rank the order and / or identify the main predicted damages 72. The BC 12 server can transmit them for visual presentation, and display for viewing by the consumer as a user interface 90 The main predicted damages 72, including treatments, such as the risk score of element 74, the rank order, the size and color indicating damage 72, which require primary surveillance, can be shown shown in a first example non-limiting in the 22C data table and / or in other non-limiting examples, as shown in the 90C user interface illustrated in figure 8 and in the 90J, 90K, 90L, 90M, 90N, 90P, 90Q, 90R, 90S user interfaces illustrated in figures 15-23. The displayed list of damages in particular 72 (such as tax refund fraud or existing credit card fraud) is prioritized and / or ranked based on these damages 72 which are predicted by algorithm 10 as most likely based on the unique characteristics of any particular violation event 70. In step 60, and as shown in figure 6, method 200 adds, for each damage 72, the risk score of element 74 for all information elements 68 determined by algorithm 10 as susceptible to this damage 72, to generate a damage risk score 76. In the example shown, algorithm 10 totals the damage risk scores 76 to generate a data breach score 80, which, in the example shown in figure 6, is a absolute data breach score 80A which has a value, in the example, of 83. In one example, algorithm 10 may include applying a modifier to the total sum of the damage risk scores 76 to generate the data breach score 80, where the modifi for example, it may be based on the number and / or types of information elements 68 exposed by the breach event 70, the number and / or the types of damage 72 associated by algorithm 10 with the information elements breached 68, in an exposure score 132 applied in the event of violation 70, etc., in such a way that the example of a totalization or sum algorithm 10 is illustrative and not limiting. The data breach score 80, which, in the illustrative example, is referred to as a
    [0026] [0026] In step 62, the process continues with a prioritized list of consumer fraud mitigation actions in particular 116, which may include, for example, actions such as obtaining a credit freeze, setting a fraud alert, or credit monitoring, which is generated by the BC 100 system using data structure 22. The mitigation actions in particular 116 identified for the particular breach event 70 are ranked to generate a set of action from the mitigation actions 116 (see figure 8) representing the relatively strongest protection against the risks and damages 72 in particular identified by the BC 100 system for the particular breach event 70. In one example, algorithm 10 uses a 22D data table, as shown in figure 11, which can be included in data structure 22, to determine a priority factor for action 136
    [0027] [0027] In step 64, the BC exits, including the BC score 80, the most likely, for example, the main expected damages 72, the prioritized mitigation actions 116 and the exposure score 132 (see the examples shown in figures 19 -23) are presented to the victim consumer through a user interface 90 and, advantageously, in a presentation format designed for the consumer's use. The examples of user interface 90 provided here are favored by organizing the BC exits in an easily understood and graphically summarized format, as opposed to ad-hoc, segmented and / or otherwise consumer advice and / or information to which a consumer may, in other circumstances, be presented from multiple sources. In addition, the exemplary user interfaces 90 that can be generated by the BC 12 server, as shown in figures 12-23, include one or more graphical user interfaces (GUIs) that include links to
    [0028] [0028] In relation to figure 3, in the example shown, a consumer starts the Data Breach Score 100 system by providing the name of a particular data breach (such as “Azure Jewelery”, 70F breach event in the example hypothetical shown in figure 3), for example, through a user interface 90, in which the “Azure Jewelery” entry is associated via the BC 22 data structure with the information elements 68A and 68I that were reported as breached in the Azure Jewelery 70F breach event, and a program-ready information form that can be designed as “a Social Security number (SSN) (element 68A) and e-mail address (element 68I)” is generated. The data breach in particular 70F (in the current example) can be stored in data structure 22, associated with the compromised information element fields in particular 68A, 68I. The BC 12 server applies algorithms 10 to the violated element fields 68A, 68I and other characteristics unique to the data breach in particular 70F (in the current example) to compute the BC outputs that include the potential damage 72 most strongly enabled by the exposure of SSN and email address, risk scores of element 74 for each combination of damage-information element, damage risk scores 76 for each damage that considers all information elements 68 violated in the particular violation event 70F , an exposure score 132, action prioritization factors 136 and / or a total BC score 80 for the Azure Jewelery 70F breach event, as illustrated in the example shown in figure 6.
    [0029] [0029] Note that, for the sake of brevity, only one
    [0030] [0030] With respect to figure 4, it should be noted that, for the sake of brevity, only a subset of the many potential risks and damages 72A… 72n are listed in the damage fields of data table 22B shown in figure 4. As an illustrative example, possible damages 72 that can be stored in the BC 22 data structure and used in the analysis of breach information and in the generation of risk outputs by the BC 10 algorithms and applications 20 include, but are not limited to, tax fraud ( including federal, state, district and municipal); new account financial fraud; new account fraud for non-financial accounts (such as utilities or cable); existing account fraud, including payment cards, deposit accounts, investment accounts, loan and mortgage accounts, insurance accounts; fraud of another account, including Internet, merchant, online shopping (such as Amazon); social media, utility; fraud on government benefits such as
    [0031] [0031] Data structure 22B represented by the table shown in figure 4 is filled with the risk score of element 74 determined for each particular pairing of an information element 68 and a damage 72, using quantitative and secondary research, and is used by algorithms 10 to compute BC risk outputs for each breach event 70 inserted in the BC 100 system and data structure 22, including publicly reported data breach events, such as, for example, breach events 70A, 70B, 70E, 70F,… 70n shown in figure 3, and to compute the BC risk outlets for an individual consumer breach event 70, for example, theft of a consumer wallet that includes information elements, such as a SSN, driver's license numbers, account numbers, etc., which can be entered into the BC 100 system by a victim consumer through a 90E user interface, as shown in figure 10. For each element of information 68, the table shown in figure 4 illustrates which particular damages 72 are most strongly (or weakly) enabled by the criminal's possession of the violated information element 68 (with examples showing an upper limit, such as
    [0032] [0032] Now, in relation to figures 5 and 6, an illustrative example that uses the fictional violation event 70 referred to in the figures as the “Violation in Azure Jewelery” event 70F is shown. Method 200 which includes flowchart 200B shown in figure 5 and which includes steps 82, 84, 86 and 88 illustrates the application of the BC 100 system in the Azure Jewelery 70F breach event to generate the risk outputs shown in the data table 22C of
    [0033] [0033] As previously described, the BC 10 algorithm can be configured to generate a relative BC score 80B, where the relative BC score 80B can be derived from the absolute BC score 80A and expressed as a value on a fixed scale , such as a scale from 0 to 50, a scale from 0 to 10, a scale from 0 to 100, etc., as shown in the examples in figure 8 and in figures 15-23, in such a way that the BC 80B score of a particular breach event 70 can be compared with the BC 80B scores generated for other breach events 70, to understand the relative risk of a breach event 70, compared to another breach event 70. In one example , data table 22C can be displayed to the victim consumer as a 90B user interface, for example, via the input / output interface 128 of a
    [0034] [0034] Now, in relation to figure 7, a flowchart 200C, which is included in the BC 200 method, is provided including steps 92 to 114, as described in figure 7, which illustrates the aspects and / or features of the algorithm BC 10 that can be used with and / or incorporated into the basic methodology illustrated by flowchart 200A of figure 2, where flowchart 200A is included in method BC 200 described herein. In one example, the BC 10 algorithm illustrated by flowchart 200C generates the BC score 80 as a relative BC score 80B, as a numerical score from 0 to
    [0035] [0035] As shown in figure 7, in step 92, the breach event 70 is inserted into the BC 100 system by the victim consumer, through a user interface 90, which can be, for example, the user interface 90C shown in figure 8 where the consumer inserts the name of the breach event 70 into a breach entry field 124, or the 90D user interface shown in figure 9 in which the consumer selects a breach entry field 124 associated with the consumer breach event 70 from a breach event menu that may include additional descriptive breach information, such as the date of the breach event 70 and / or the information elements 68 breached during the breach event 70 In other examples shown in figures 12-23, user interface 90 may include a breach entry field 124 configured as a test box, a search field and / or a drop-down menu that lists known breach events 70 included in the si system BC 100. The victim consumer can, for example, obtain the violation information from a notification of the violation event 70 provided by a reporting entity. Optionally, the consumer can insert, in step 114, the elements
    [0036] [0036] After receiving the input from the consumer through any of the steps 92 and 114, the method continues in step 94, in which, using the information that identifies the violation event 70, the BC 12 server, for example, through a BC 20 application, access the BC 22 data structure to retrieve the information associated with the breach event
    [0037] [0037] In step 96, the BC 20 application, for each of the information elements 68 that was compromised by the violation event 70, recovers a risk score of element 74 for each potential damage associated in the data structure 22 with the violated information element 68, as described for figure 4, in which a risk score of element 74 is generated for each pair of information-damage element. The risk score of element 74 is generated by the BC 10 algorithm.
    [0038] [0038] In each of the steps 98, 102 and 104, the BC 10 algorithm can use the data breach information retrieved from the BC 22 data structure to modify, weight and / or filter the risk scores of element 74 generated in step 96, before generating a damage risk score 76 for each potential damage 72 identified.
    [0039] [0039] For example, in step 98, the BC 10 algorithm can apply a weighted percentage to take into account the general availability of the exposed information element 68, where the general availability is determined from quantitative research and can reflect availability overview of the exposed information element 68 from sources of non-violation. For example, for an exposed information item 68 of a home address, the factor applied may reflect the general availability of the consumer's home address from available public records and / or other publicly available information resources, such as directories
    [0040] [0040] For example, in step 102, the BC 10 algorithm can apply a weighted percentage to take into account the overall prevalence of each potential damage 72 associated in the data structure 22 with the violated information element 68.
    [0041] [0041] For example, in step 104, the BC 10 algorithm can apply a weighted percentage to take into account the expected personal damage from each potential damage 72 to the individual consumer. The expected personal damage can be quantified in data structure 22, for example, in the potential financial loss in dollars due to fraud, etc., or as an expense incurred in implementing the mitigation actions 116 and / or time in the hours lost to contain , prevent and / or rectify the damage from damage 72 or perform mitigation actions 116. The expected personal damage can be quantified, for example, as the result of quantitative research collected from multiple resources and / or reporting entities and / or supplemented from publicly available information and / or information collected by the BC 100 system from victim consumers via user interface 90, or modified using an exposure score 132 determined for the particular violation 70. In one example, demographic and / or behavioral information collected from and / or about the individual victim-consumer, in the manner previously described here, and / or other information in the risk profile of the consumer-victim stored on the BC 12 server, including, for example, other breach events 70 for which the victim-victim was victimized, can be taken into account in an algorithm 10 in the estimation and / or determination of the expected personal damage of each potential harm 72 to the individual victim consumer.
    [0042] [0042] In step 106, algorithm 10 calculates a damage risk score 76 for each potential damage 72 identified for the breached information elements 68, where the numerical value of the damage risk score 76 represents the predicted probability of that the rape victim experiences the particular damage 72 for which the damage risk score 76
    [0043] [0043] In step 108, a total BC score of 80 is generated, representing the risk associated with all the particular potential damages 72 associated with the BC 10 algorithms with all the information elements in particular 68 that were compromised by the violation event 70 In the example of the 90C user interface shown in figure 8, the BC score 80 can be presented as a numerical value, and in a graphical representation 122. The numerical value of the BC score can be a relative BC score 80B, for example, expressed in relation to a fixed BC scale. In relation to the example shown in figure 8, the value of the relative BC score 80B is 72/100, where 100 is the upper limit of the BC 122A scale, and is displayed in the 90C user interface as both a numerical value “72” and graphically on a BC 122A scale, which can be color-coded, for example, Red-Yellow-Green, based on the magnitude and / or risk associated with the BC 80B score that is displayed. In relation to the examples shown in figures 15-23, the value of the relative BC score 80C is expressed on a scale that includes an upper limit of “10”, and is displayed in the user interface 90 as much as a numerical value shown in an icon circular as well as graphically on a 122B color-coded BC scale, which can be color-coded, for example, Yellow-Orange-Red, based on the magnitude of and / or the risk associated with the BC 80C score that is displayed. Regarding the example of the 90Q, 90R user interfaces shown in the
    [0044] [0044] In step 110, the BC 10 algorithm, which uses the risk scores of element 74 of the violated information elements 68, determines the recommended consumer action steps, including, for example, mitigation actions 116, as illustrated by the examples shown in figure 8 and figures 15-23. The steps of consumer action and / or mitigation actions 116 are based on information that can be stored in the BC 22 data structure, including, for example, the results of research interviews, qualitative and quantitative input and / or exams impartial industry experts working in the field of protecting identity-related consumer damage.
    [0045] [0045] In step 112, the BC 12 server transmits the risk outputs generated by the BC 10 algorithms and / or BC 20 applications to the victim consumer, through a user interface 90 configured by the BC system
    [0046] [0046] In a non-limiting example, user interface 90 may include one or more graphical user interfaces (GUI) that can be used by the victim consumer to interact with user interface 90 to view information related to the event violation 70 and / or BC risk exits and / or action mitigation steps 116. As a non-limiting example, user interfaces 90, including 90a through 90S, may include one or more of the graphical user interfaces 118 , 120, 122, 124, 132, 134, as described herein with further details. The term "graphical user interface" or "GUI" should be interpreted widely and may include, for example, one or more of graphic icons, links, buttons, switches, input fields, widgets, menus, lists, text windows, dialog boxes, etc. The victim consumer can act on the GUI, for example, through an entry in the GUI provided by a touch applied to a touch screen that displays the user interface 90, an entry from a keyboard, which can be a virtual keyboard displayed on the input / output interface 128, an input from a pointing device, such as a mouse, pointing stick, a voice input, etc. In an illustrative example shown in figure 8, the 90C user interface can include multiple GUIs. As shown in the examples in figures 8, 9, 12-14 and 23, a user can insert a violation descriptor 70 in an input field 124, to search for a violation event, or a plurality of violation events. As a non-limiting example, input field 124 may be a search field, may include a drop-down menu, be linked to a pop-up screen that lists breach events 70, which includes the BC 22 data structure, etc. In the example shown in figure 8, a user entry in the field that displays the 70E violation descriptor,
    [0047] [0047] For example, a user entry in a GUI associated with the field that displays “Your Highest Risks” can act on the 90C user interface to display additional information about the damage list 72, for example, by expanding the window to show the full list of potential damages 72 associated with the ACME 70E breach event by the BC system
    [0048] [0048] For example, a user entry in a GUI associated with the field that displays “Your To-Do List” (see figure 8) or the field that displays “Main Action Steps” (see figures 15-23) user interface 90 can act to display additional information about the list of mitigation actions 116, for example, by expanding the window to show the full list of mitigation actions 116 recommended to the consumer-victim of the ACME 70E violation event by the BC 100 system. In one example, a field for a particular “to do” item, such as the field that lists the mitigation action 116 “Define fraud alerts through a bureau
    [0049] [0049] User interfaces 90, in the examples shown, can be actuated by the consumer to export at least a part of the BC violation information by the actuation by the consumer of the “Export” 120 icon in the present illustration. In the example of the user interface
    [0050] [0050] Figures 12-14 illustrate the non-limiting examples of user interfaces 90F, 90G, 90H through which a consumer can access the BC 100 system and the BC risk outlets previously described here. The example shown in figure 12 illustrates a BC 90F interface that can be used to provide introductory information to a consumer, which can include a breach entry field 124 to enter the name of a breach to be searched for, or to operate a pull-down menu. of breach events 70 for which breach information can be retrieved from the BC 100 system. The BC 90F user interface may include one or more actuable links 118 to connect the consumer to third party resources, including, for example, reporting servers 40 and / or resource servers 50, from which the consumer can obtain additional breach information. In an illustrative example, the BC user interface shown in figure 13 can include links from GUI 118 to third-party providers, such as financial or healthcare institutions, who can offer subscription and / or sponsored access to the BC 100 system
    [0051] [0051] Figures 15-23 are provided as illustrative examples of the user interfaces 90G, 90H, 90J, 90K, 90L, 90M, 90N, 90P, 90Q, 90R, and 90S that can be generated by the BC 12 server and transmitted in a user device 30 for display to and / or access by a victim consumer, including the BC risk outlets previously described herein. As shown in figures 15-23, BC risk exits may include, for example, a breach event descriptor 70, a Breach Clarity ™ score 80 shown numerically and / or graphically 80C, a listing of information elements 68 exposed by the breach event 70, a list of damages and / or risks 72 that can also be shown as a risk distribution chart 134 which, in the illustrative examples, is a pie chart or segmented annular chart (donut), a listing of mitigation actions 116 that are ordered by the effectiveness rating in protecting the consumer from damage, an exposure score 132 based on the type and / or nature of the breach event 70 (hacking, unauthorized access, theft, Internet exposure , etc.), and one or more GUIs or links that can be operated by the consumer through user device 30 to access affiliated Internet pages, Internet pages, resources, third party providers, etc., including, by and example, one or more reporting and / or resource servers 40, 50.
    [0052] [0052] The examples provided here are not limiting. For example, the algorithms 10 described here are illustrative and may include additional factors, operands and / or operators collected from the quantitative research that was conducted in the development of the data breach system 100. For example, a BC 10 algorithm can be configured to include a persistence factor for each information item 68, where
    [0053] [0053] The detailed description and the drawings or figures are for support and description of the description, but the scope of the description is defined exclusively by the claims. Although some of the best ways and other modalities for carrying out the claimed description have been described in detail, several alternative designs and modalities exist to practice the description defined in the attached claims. Furthermore, the
    54/54 modalities shown in the drawings or the characteristics of the various modalities mentioned in the present description should not necessarily be understood as modalities independent of each other.
    Instead, it is possible that each of the characteristics described in one of the examples of a modality can be combined with one or a plurality of other desired characteristics of other modalities, resulting in other modalities not described in words or by reference to the drawings.
    In this way, such other modalities fall within the scope of the attached claims.
    权利要求:
    Claims (20)
    [1]
    1. Method, characterized by the fact that it comprises: filling, through a server, a data structure with the violation information; where the breach information includes: a plurality of information elements; and a plurality of damages; wherein each information element of the plurality of information elements is paired with each damage of the plurality of damage to generate a plurality of information element-damage data pairs; generate, using an algorithm, an element risk score for each respective information damage-element pair of the plurality of information damage-element data pairs; and to associate, in the data structure, the risk score of the element with the respective data element-damage data pair.
    [2]
    2. Method according to claim 1, characterized by the fact that the data breach information additionally includes: a breach event descriptor, in which the breach descriptor identifies a breach event; and at least one element of information breached, wherein the at least one element of information breached is a respective element of information from the plurality of information elements that was compromised by the breach event; the method additionally comprising: receiving, through the server, the descriptor of the violation event and at least one element of information violated; associate, in the data structure, the breach event descriptor with at least one breached information element; and associating, using the data structure, each information damage-element pair from the plurality of information damage-element data pairs that includes the at least one breached information element with the breach event descriptor.
    [3]
    3. Method according to claim 2, characterized by the fact that it additionally comprises: using the algorithm, to generate a damage risk score for the respective damage of each data element-damage data pair associated with the item descriptor. violation event; associate, using the data structure, the damage risk score for each damage with the violation event descriptor; and store in the data structure the score of the risk of damage associated with the descriptor of the violation event.
    [4]
    4. Method according to claim 3, characterized by the fact that it additionally comprises: generating, using the algorithm, a data breach score for the breach event; and where the generation of the data breach score includes adding the damage risk scores of the respective damages of each data element-damage data pair associated with the breach event descriptor to generate the data breach score.
    [5]
    5. Method according to claim 4, characterized by the fact that the data breach score is calculated by the algorithm with an absolute value.
    [6]
    6. Method according to claim 4, characterized by the fact that the data breach score is calculated by the algorithm as a relative value.
    [7]
    7. Method according to claim 6, characterized by the fact that it additionally comprises: generating the relative value, using the algorithm, by applying at least one of a scaling factor and a modifier in the data breach score.
    [8]
    8. Method according to claim 4, characterized by the fact that it additionally comprises: transmitting, through the server, the data breach score to a user interface; and where the user interface communicates with the server.
    [9]
    9. Method according to claim 8, characterized by the fact that it additionally comprises: using, using the algorithm, at least one mitigation action to mitigate at least one damage associated with the violation event descriptor; and transmit at least one damage and at least one mitigation action to the user interface.
    [10]
    10. Method according to claim 9, characterized by the fact that it further comprises: associating, in the data structure, at least one mitigation action with at least one damage to form a damage mitigation action data pair ; determine, using the algorithm, a prioritization factor for the damage mitigation action data pair.
    [11]
    11. Method according to claim 10, characterized by the fact that the at least one mitigation action includes a plurality of mitigation actions; determine, using the algorithm, a respective prioritization factor for each respective mitigation action of the plurality of mitigation actions; and associate, in the data structure, the respective prioritization factor with each respective mitigation action.
    [12]
    12. Method according to claim 11, characterized by the fact that it additionally comprises: compiling, using the algorithm, a listing of the plurality of mitigation actions;
    in which each respective mitigation action is ordered in the list according to the respective prioritization factor associated with the respective mitigation action.
    [13]
    13. Method according to claim 9, characterized by the fact that it additionally comprises: associating, in the data structure, a user interface with at least one mitigation action; where the user interface is actionable to initiate at least one mitigation action.
    [14]
    14. Method according to claim 12, characterized by the fact that it additionally comprises: providing, through the server, the user interface for a user device.
    [15]
    15. Method according to claim 2, characterized by the fact that it additionally comprises: generating, using the algorithm, an exposure score for the violation event; and associate, in the data structure, the exposure score with the descriptor of the violation event.
    [16]
    16. Device, characterized by the fact that it comprises a computing device that has a processor and a non-transitory memory, the non-transitory memory storing instructions executable by the processor in such a way that the device is configured to: fill a data structure with infringement information; where the breach information includes: a plurality of information elements; and a plurality of damages; wherein each information element of the plurality of information elements is paired with each damage of the plurality of damage to generate a plurality of information element-damage data pairs; generate, using an algorithm, an element risk score for each respective information damage-element pair of the plurality of information damage-element data pairs; and to associate, in the data structure, the risk score of the element with the respective data element-damage data pair.
    [17]
    17. Apparatus according to claim 16, characterized by the fact that the data breach information additionally includes: a breach event descriptor, in which the breach descriptor identifies a breach event; and at least one element of information breached, wherein the at least one element of information breached is a respective element of information from the plurality of information elements that was compromised by the breach event; in which the device is additionally configured to: receive, through the server, the descriptor of the violation event and the at least one element of information violated; associate, in the data structure, the breach event descriptor with at least one breached information element; and associating, using the data structure, each information damage-element pair from the plurality of information damage-element data pairs that includes the at least one breached information element with the breach event descriptor.
    [18]
    18. Apparatus according to claim 17, characterized by the fact that it is additionally configured to: generate, using the algorithm, a damage risk score for the respective damage of each data element-damage data pair associated with the violation event descriptor; associate, using the data structure, the damage risk score for each damage with the violation event descriptor; and store in the data structure the score of the risk of damage associated with the descriptor of the violation event.
    [19]
    19. Apparatus according to claim 18, characterized by the fact that it is additionally configured to: generate, using the algorithm, a data breach score for the breach event; and where the generation of the data breach score includes adding the damage risk scores of the respective damages of each data element-damage data pair associated with the breach event descriptor to generate the data breach score.
    [20]
    20. Apparatus according to claim 17, characterized by the fact that it is additionally configured to: generate, using the algorithm, an exposure score for the violation event; and associate, in the data structure, the exposure score with the descriptor of the violation event.
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20120143650A1|2010-12-06|2012-06-07|Thomas Crowley|Method and system of assessing and managing risk associated with compromised network assets|
    US10318740B2|2014-07-30|2019-06-11|Entit Software Llc|Security risk scoring of an application|
    US10341376B2|2014-12-29|2019-07-02|Guidewire Software, Inc.|Diversity analysis with actionable feedback methodologies|US10592692B2|2016-06-10|2020-03-17|OneTrust, LLC|Data processing systems for central consent repository and related methods|
    US11004125B2|2016-04-01|2021-05-11|OneTrust, LLC|Data processing systems and methods for integrating privacy information management systems with data loss prevention tools or other tools for privacy design|
    US11244367B2|2016-04-01|2022-02-08|OneTrust, LLC|Data processing systems and methods for integrating privacy information management systems with data loss prevention tools or other tools for privacy design|
    US11188615B2|2016-06-10|2021-11-30|OneTrust, LLC|Data processing consent capture systems and related methods|
    US11138242B2|2016-06-10|2021-10-05|OneTrust, LLC|Data processing systems and methods for automatically detecting and documenting privacy-related aspects of computer software|
    US11222309B2|2016-06-10|2022-01-11|OneTrust, LLC|Data processing systems for generating and populating a data inventory|
    US10803202B2|2018-09-07|2020-10-13|OneTrust, LLC|Data processing systems for orphaned data identification and deletion and related methods|
    US10169609B1|2016-06-10|2019-01-01|OneTrust, LLC|Data processing systems for fulfilling data subject access requests and related methods|
    US11210420B2|2016-06-10|2021-12-28|OneTrust, LLC|Data subject access request processing systems and related methods|
    US10853501B2|2016-06-10|2020-12-01|OneTrust, LLC|Data processing and scanning systems for assessing vendor risk|
    US10909488B2|2016-06-10|2021-02-02|OneTrust, LLC|Data processing systems for assessing readiness for responding to privacy-related incidents|
    US10846433B2|2016-06-10|2020-11-24|OneTrust, LLC|Data processing consent management systems and related methods|
    US11087260B2|2016-06-10|2021-08-10|OneTrust, LLC|Data processing systems and methods for customizing privacy training|
    US11188862B2|2016-06-10|2021-11-30|OneTrust, LLC|Privacy management systems and methods|
    US11138299B2|2016-06-10|2021-10-05|OneTrust, LLC|Data processing and scanning systems for assessing vendor risk|
    US11134086B2|2016-06-10|2021-09-28|OneTrust, LLC|Consent conversion optimization systems and related methods|
    US10467432B2|2016-06-10|2019-11-05|OneTrust, LLC|Data processing systems for use in automatically generating, populating, and submitting data subject access requests|
    US11074367B2|2016-06-10|2021-07-27|OneTrust, LLC|Data processing systems for identity validation for consumer rights requests and related methods|
    US10565161B2|2016-06-10|2020-02-18|OneTrust, LLC|Data processing systems for processing data subject access requests|
    US11146566B2|2016-06-10|2021-10-12|OneTrust, LLC|Data processing systems for fulfilling data subject access requests and related methods|
    US10839102B2|2016-06-10|2020-11-17|OneTrust, LLC|Data processing systems for identifying and modifying processes that are subject to data subject access requests|
    US11025675B2|2016-06-10|2021-06-01|OneTrust, LLC|Data processing systems and methods for performing privacy assessments and monitoring of new versions of computer code for privacy compliance|
    US11222142B2|2016-06-10|2022-01-11|OneTrust, LLC|Data processing systems for validating authorization for personal data collection, storage, and processing|
    US11222139B2|2016-06-10|2022-01-11|OneTrust, LLC|Data processing systems and methods for automatic discovery and assessment of mobile software development kits|
    US11144675B2|2018-09-07|2021-10-12|OneTrust, LLC|Data processing systems and methods for automatically protecting sensitive data within privacy management systems|
    US10878127B2|2016-06-10|2020-12-29|OneTrust, LLC|Data subject access request processing systems and related methods|
    US10997315B2|2016-06-10|2021-05-04|OneTrust, LLC|Data processing systems for fulfilling data subject access requests and related methods|
    US11057356B2|2016-06-10|2021-07-06|OneTrust, LLC|Automated data processing systems and methods for automatically processing data subject access requests using a chatbot|
    US11157600B2|2016-06-10|2021-10-26|OneTrust, LLC|Data processing and scanning systems for assessing vendor risk|
    US10606916B2|2016-06-10|2020-03-31|OneTrust, LLC|Data processing user interface monitoring systems and related methods|
    US10803200B2|2016-06-10|2020-10-13|OneTrust, LLC|Data processing systems for processing and managing data subject access in a distributed environment|
    US10873606B2|2016-06-10|2020-12-22|OneTrust, LLC|Data processing systems for data-transfer risk identification, cross-border visualization generation, and related methods|
    US10796260B2|2016-06-10|2020-10-06|OneTrust, LLC|Privacy management systems and methods|
    US10284604B2|2016-06-10|2019-05-07|OneTrust, LLC|Data processing and scanning systems for generating and populating a data inventory|
    US11144622B2|2016-06-10|2021-10-12|OneTrust, LLC|Privacy management systems and methods|
    US11228620B2|2016-06-10|2022-01-18|OneTrust, LLC|Data processing systems for data-transfer risk identification, cross-border visualization generation, and related methods|
    US11023842B2|2016-06-10|2021-06-01|OneTrust, LLC|Data processing systems and methods for bundled privacy policies|
    US10607028B2|2016-06-10|2020-03-31|OneTrust, LLC|Data processing systems for data testing to confirm data deletion and related methods|
    US10783256B2|2016-06-10|2020-09-22|OneTrust, LLC|Data processing systems for data transfer risk identification and related methods|
    US10896394B2|2016-06-10|2021-01-19|OneTrust, LLC|Privacy management systems and methods|
    US10740487B2|2016-06-10|2020-08-11|OneTrust, LLC|Data processing systems and methods for populating and maintaining a centralized database of personal data|
    US11227247B2|2016-06-10|2022-01-18|OneTrust, LLC|Data processing systems and methods for bundled privacy policies|
    US11100444B2|2016-06-10|2021-08-24|OneTrust, LLC|Data processing systems and methods for providing training in a vendor procurement process|
    US10282559B2|2016-06-10|2019-05-07|OneTrust, LLC|Data processing systems for identifying, assessing, and remediating data processing risks using data modeling techniques|
    US10997318B2|2016-06-10|2021-05-04|OneTrust, LLC|Data processing systems for generating and populating a data inventory for processing data access requests|
    US10503926B2|2016-06-10|2019-12-10|OneTrust, LLC|Consent receipt management systems and related methods|
    US10592648B2|2016-06-10|2020-03-17|OneTrust, LLC|Consent receipt management systems and related methods|
    US10949565B2|2016-06-10|2021-03-16|OneTrust, LLC|Data processing systems for generating and populating a data inventory|
    US10909265B2|2016-06-10|2021-02-02|OneTrust, LLC|Application privacy scanning systems and related methods|
    US10282700B2|2016-06-10|2019-05-07|OneTrust, LLC|Data processing systems for generating and populating a data inventory|
    US11200341B2|2016-06-10|2021-12-14|OneTrust, LLC|Consent receipt management systems and related methods|
    US10565236B1|2016-06-10|2020-02-18|OneTrust, LLC|Data processing systems for generating and populating a data inventory|
    US10885485B2|2016-06-10|2021-01-05|OneTrust, LLC|Privacy management systems and methods|
    US11151233B2|2016-06-10|2021-10-19|OneTrust, LLC|Data processing and scanning systems for assessing vendor risk|
    US10944725B2|2016-06-10|2021-03-09|OneTrust, LLC|Data processing systems and methods for using a data model to select a target data asset in a data migration|
    US11038925B2|2016-06-10|2021-06-15|OneTrust, LLC|Data processing systems for data-transfer risk identification, cross-border visualization generation, and related methods|
    US11238390B2|2016-06-10|2022-02-01|OneTrust, LLC|Privacy management systems and methods|
    WO2021081516A1|2019-10-26|2021-04-29|Breach Clarity, Inc.|Data breach system and method|
    US10606459B2|2018-02-26|2020-03-31|Capital One Services, Llc|Methods and systems for dynamic monitoring through graphical user interfaces|
    US20200143500A1|2018-11-05|2020-05-07|Wells Fargo Bank, N.A.|Systems and methods for providing notifications regarding data breaches|
    US10991231B1|2019-10-10|2021-04-27|Bank Of America Corporation|Silent alarms for economic duress|
    法律状态:
    2021-11-23| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    2021-12-28| B25A| Requested transfer of rights approved|Owner name: SONTIQ, INC. (US) |
    优先权:
    申请号 | 申请日 | 专利标题
    US201762548656P| true| 2017-08-22|2017-08-22|
    US62/548,656|2017-08-22|
    PCT/US2018/047237|WO2019040443A1|2017-08-22|2018-08-21|Data breach score and method|
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