Book Chapter

Information-theoretic privacy through chaos synchronization and optimal additive noise

C Murguia, I Shames, F Farokhi, D Nešić

Privacy in Dynamical Systems | Springer | Published : 2020

Abstract

We study the problem of maximizing privacy of data sets by adding random vectors generated via synchronized chaotics oscillators. In particular, we consider the setup where information about data sets, queries, is sent through public (unsecured) communication channels to a remote station. To hide private features (specific entries) within the data set, we corrupt the response to queries by adding random vectors.We send the distorted query (the sum of the requested query and the random vector) through the public channel. The distribution of the additive random vector is designed to minimize the mutual information (our privacy metric) between private entries of the data set and the distorted q..

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