Decision Support for Sharing Data Using Differential Privacy

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Mark F. St. John, Grit Denker, Peeter Laud, Karsten Martiny, Alisa Pankova, Dusko Pavlovic

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 View presentation:2021-10-27T15:40:00ZGMT-0600Change your timezone on the schedule page
2021-10-27T15:40:00Z

Exemplar figure, described by caption below — A screenshot of the proposed differential privacy policy tool. For the given levels of trust, data sensitivity, and the maximum tolerated risk, the tool proposes the minimum recommended noise. The user can explore the graph and e.g. choose a slightly smaller noise that still provides a similar level of risk.

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Abstract

Owners of data may wish to share some statistics with others, but they may be worried of privacy of the underlying data. An effective solution to this problem is to employ provable privacy techniques, such as differential privacy, to add noise to the statistics before releasing them. This protection lowers the risk of sharing sensitive data with more or less trusted data sharing partners. Unfortunately, applying differential privacy in its mathematical form requires one to fix certain numeric parameters, which involves subtle computations and expert knowledge that the data owners may lack.