System and method for objective quantification and mitigation of privacy risk

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;
• 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;
• and
• applying the mitigation techniques to the generated dataset based on the mitigating measures.

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:

• receive, 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;
• calculate, 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;
• compute, 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;
• quantify, via a contextual sub-engine, contextual factors via weighting and producing a single context-centric score;
• identify, via a recommendation engine, mitigating measures to reduce the privacy-risks by taking in to account the factors that are contributing to higher risk;
• and
• apply the mitigation techniques to the generated dataset based on the mitigating measures.

A system for objective quantification and mitigation of privacy-risks within at least one dataset, the system comprising:

• a processor executing one or more engines coupled to an interface for identifying receiving at least one synthetic 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 at least one synthetic dataset, risk scoring and risk mitigation settings;
• a scoring engine including:
• a uniqueness sub-engine for determining whether each of the records in the at least one synthetic 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 the at least one synthetic datasets to another dataset 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 the datasets, the captured information used to determine the level of re-identification risk the at least one synthetic dataset contains;
• 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 considering the factors that are contributing to higher risk, the mitigating measures defined in the configuration file or selected from the list of recommendations made by the recommendation engine,
• the recommendation engine applying the mitigation techniques to the synthetic dataset based on the mitigating measures defined in the configuration file or selected from the list of recommendations made by the recommendation engine.