Truata Limited

Imran Khan

Maurice Coyle

Michael Fenton

Barry Smyth

Noel Rogers

Aydin Ulas

Dave Fagan

A system and method for guiding privacy-enhancing transformations are described. The system and method include a recommendation engine configured to identify sets of transformations to mitigate a privacy risk below a user specified threshold specified in-terms of privacy-risk score for a given input dataset while keeping the utility of the dataset above the user-specified utility threshold specified in-terms of utility score. A simulation engine configured to simulate the identified set of transformations from the recommendation engine on the dataset to determine the optimal application of the plurality of transformations for maximizing the utility of the dataset, and output device to provide the optimized dataset with the privacy risk score and utility score.

System and method for guiding privacy-enhancing transformations

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.

System and method for objective quantification and mitigation of privacy risk

A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed. These include an input-output (IO) interface for receiving at least one input dataset, 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, 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.

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Dublin, Ireland

Imran Khan