# US 2021/0279367 A1 · System and method for objective quantification and mitigation of privacy risk > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279367 A1 - Document ID: US20210279367A1 - Status: Published application - Kind: A1 - Application number: 16/813,327 - Filed: 2020-03-09 - Published: 2021-09-09 - Earliest claimed: 2020-03-09 - Assignees: Truata Limited - Applicants: Truata Limited - Inventors: Imran Khan; Michael Fenton; Maurice Coyle - Web page: https://patentgrep.com/patent/US20210279367A1 ## CPC - G06F 21/6254: {by anonymising data, e.g. decorrelating personal data from the owner's identification} - G06F 16/168: {Details of user interfaces specifically adapted to file systems, e.g. browsing and visualisation, 2d or 3d GUIs (query results presentation G06F16/156)} - G06F 16/221: {Column-oriented storage; Management thereof} - G06F 21/577: {Assessing vulnerabilities and evaluating computer system security} - G06Q 30/0631: {Recommending goods or services} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed. The system and method include an input-output (IO) interface for receiving at least one input dataset, on at least one of which a measurement of the risk is to be performed, and a configuration file governing the anonymization; a scoring engine including: a uniqueness sub-engine for determining uniqueness scores at data and data-subject level across both individual columns and combinations of columns; a similarity sub-engine that calculates the overlap, reproducibility and similarity by comparing all columns and subsets of columns between at least two datasets, where one dataset is the original version and the other one is transformed, modified, anonymized or synthetic version of original dataset; a statistical sub-engine that calculates statistics that are an indication of privacy-risks and re-identification risks; a contextual sub-engine for quantifying contextual factors by considering weighted approaches and producing a single context-centric score; and a recommendation engine identifying mitigating measures to reduce the privacy-risks by taking in to account the factors that are contributing to higher risk. ## Claims ### Claim 1 A system for objective quantification and mitigation of privacy-risks within at least one dataset, the system comprising: an input-output (IO) interface for receiving at least one input dataset, on at least one of which a measurement of the risk is to be performed, and a configuration file governing the specifications of the dataset(s), risk scoring and risk mitigation settings; a scoring engine including: a uniqueness sub-engine for determining whether each of the records in a dataset is unique in its entirety or with regards to subsets of columns that comprise the record; a similarity sub-engine that compares entire records and subsets of columns from at least two input datasets to determine the level of similarity in terms of data reproduction or overlap that exists; a statistical sub-engine that measures statistical characteristics that capture information about input datasets, the captured information used to determine the level of re-identification risk the input datasets contain; and a contextual sub-engine for quantifying contextual factors via weighting and producing a single context-centric score; and a recommendation engine identifying mitigating measures to reduce the privacy-risks by taking into account the factors that are contributing to higher risk. ### Claim 2 (depends on 1) The system of claim 1, wherein the scoring engine computes at least one privacy-risk score and produces a risk report by accounting for contextual factors and data-centric features in the input dataset. ### Claim 3 (depends on 1) The system of claim 1, wherein the scoring engine composes the various output scores into a single privacy-risk score. ### Claim 4 (depends on 1) The system of claim 1, wherein the recommendation engine provides a risk-based recommendation to reduce the privacy-risks in the input dataset. ### Claim 5 (depends on 1) The system of claim 1, wherein the scoring engine computes scores for at least one of a synthetic dataset, an anonymized dataset, a raw dataset, and a row-level datasets. ### Claim 6 (depends on 1) The system of claim 1 wherein the uniqueness sub-engine uses an optimization step to identify subsets of columns that have high resolving power relative to the data subject. ### Claim 7 (depends on 1) The system of claim 1 wherein the IO interface outputs the risk output for the at least one input dataset. ### Claim 8 (depends on 1) The system of claim 1 wherein the recommendation engine provides actions to mitigate each privacy-risk identified by the scoring engine. ### Claim 9 (depends on 1) The system of claim 1 wherein the system minimizes overt re-identification risks. ### Claim 10 (depends on 9) The system of claim 9 wherein the re-identification risks include potential risks specified in the configuration file. ### Claim 11 (depends on 1) The system of claim 1 wherein the recommendation engine applies mitigation techniques to the dataset based on the mitigating measures defined in the config file or selected from the list of recommendations made by the recommendation engine. ### Claim 12 (depends on 11) The system of claim 11 wherein the mitigation techniques include at least one of deletion, multiplication, redaction suppression, and fuzzing, generalization, noise addition, aggregation and masking. ### Claim 13 A method for objective quantification and mitigation of privacy-risks of a dataset, the method comprising: receiving, via an input-output (IO) interface, at least one input dataset, on at least one of which a measurement of the risk is to be performed, and a configuration file governing the risk assessment; determining, via a uniqueness sub-engine, whether each of the records in a dataset is unique, where uniqueness is computed at data subject-level, or data-level by considering only one column or a combination of several columns; calculating, via a similarity sub-engine, the amount of overlap by comparing entire records and subsets of columns from at least two input datasets to determine the level of similarity in terms of data overlap that exists; computing, via a statistical sub-engine, the statistical characteristics that capture information about the at least one input dataset to determine the level of re-identification risk in the input datasets; quantifying, via a contextual sub-engine, contextual factors via weighting and producing a single context-centric score; and identifying, via a recommendation engine, mitigating measures to reduce the privacy-risks by taking in to account the factors that are contributing to higher risk. ### Claim 14 (depends on 13) The method of claim 13, further comprising computing at least one privacy-risk score and producing a risk report by accounting for contextual factors and datacentric features in the input dataset. ### Claim 15 (depends on 13) The method of claim 13 wherein the uniqueness sub-engine uses an optimization step to identify subsets of columns that have high resolving power relative to the data subject. ### Claim 16 (depends on 13) The method of claim 13, further composing the various output scores into a single privacy-risk score. ### Claim 17 (depends on 13) The method of claim 13, wherein the recommendation engine provides at least one risk-based recommendation to reduce the privacy-risks in the input dataset. ### Claim 18 (depends on 13) The method of claim 13, wherein the IO interface outputs the risk output for the at least one input dataset. ### Claim 19 (depends on 13) The method of claim 13, wherein the recommendation engine applies mitigation techniques to the generated dataset based on the mitigating measures. ### Claim 20 A non-transitory computer readable medium storing a program for objective quantification and mitigation of privacy-risks of a dataset, wherein the program, when executed by a processor, causes the processor to: receiving, via an input-output (IO) interface, at least one input dataset, on at least one of which a measurement of the risk is to be performed, and a configuration file governing the risk assessment; determine, via a uniqueness sub-engine, the level of uniqueness within the dataset by considering one column or a combination of columns using subject-level and data-level uniqueness metrics; calculating, via a similarity sub-engine, the amount of overlap by comparing entire records and subsets of columns from at least two input datasets to determine the level of similarity in terms of data overlap that exists; computing, via a statistical sub-engine, the statistical characteristics that capture information about the at least one input dataset to determine the level of re-identification risk in the input datasets; quantifying, via a contextual sub-engine, contextual factors via weighting and producing a single context-centric score; and identifying, via a recommendation engine, mitigating measures to reduce the privacy-risks by taking in to account the factors that are contributing to higher risk. ### Claim 21 (depends on 20) The non-transitory computer readable medium of claim 20, further comprising computing at least one privacy-risk score and producing a risk report by accounting for contextual factors and datacentric features in the input dataset. ### Claim 22 (depends on 20) The non-transitory computer readable medium of claim 20, further composing the various output scores into a single privacy-risk score.