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Differential Privacy via a Truncated and Normalized Laplace Mechanism

Journal of Computer Science & Technology 2022年第2期37卷 369-388页

作者： William Croft Jorg-Rudiger Sack Wei Shi School of Computer Science Carleton UniversityOttawa K1S 5B6Canada School of Information Technology Carleton UniversityOttawa K1S 5B6Canada

When querying databases containing sensitive information,the privacy of individuals stored in the database has to be *** guarantees are provided by differentially private mechanisms which add controlled noise to the query ***,most such mechanisms do not take into consideration the valid range of the query being ***,noisy responses that fall outside of this range may potentially be *** rectify this and therefore improve the utility of the mechanism,the commonly-used Laplace distribution can be truncated to the valid range of the query and then ***,such a data-dependent operation of normalization leaks additional information about the true query response,thereby violating the differential privacy ***,we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace *** adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint *** provide derivations of the optimal scaling parameter(i.e.,the minimal value that preserves differential privacy)for each class or provide an approximation *** a result of this work,one can use the Laplace distribution to answer queries in a range-adherent and differentially private *** demonstrate the benefits of our proposed method of normalization,we present an experimental comparison against other range-adherent *** show that our proposed approach is able to provide improved utility over the alternative mechanisms.

关键词： differential privacy Laplace mechanism query range constraint

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