Guaranteeing perfect data privacy seems to be incompatible with the economical and scientific opportunities provided by extensive data collection and processing. This paper tackles this challenge by studying how to disclose latent features of data sets without compromising the privacy of individual data samples. We leverage counter-intuitive properties of the multivariate statistics of data samples, and propose a technique to disclose collective properties of data sets while keeping each data sample confidential. For a given statistical description of the data set, we show how to build an optimal disclosure strategy/mapping using linear programming techniques. We provide necessary and sufficient conditions that determine when our approach is feasible, and illustrate the optimal solution in some simple scenarios. We observe that the disclosure strategy may be independent of the latent feature in some scenarios, for which explicit expressions for the performance are provided.
Latent feature disclosure under perfect sample privacy / Rassouli, B.; Rosas, F.; Gunduz, D.. - (2019), pp. 1-7. (Intervento presentato al convegno 10th IEEE International Workshop on Information Forensics and Security, WIFS 2018 tenutosi a The Hong Kong Polytechnic University, hkg nel 2018) [10.1109/WIFS.2018.8630780].
Latent feature disclosure under perfect sample privacy
D. Gunduz
2019
Abstract
Guaranteeing perfect data privacy seems to be incompatible with the economical and scientific opportunities provided by extensive data collection and processing. This paper tackles this challenge by studying how to disclose latent features of data sets without compromising the privacy of individual data samples. We leverage counter-intuitive properties of the multivariate statistics of data samples, and propose a technique to disclose collective properties of data sets while keeping each data sample confidential. For a given statistical description of the data set, we show how to build an optimal disclosure strategy/mapping using linear programming techniques. We provide necessary and sufficient conditions that determine when our approach is feasible, and illustrate the optimal solution in some simple scenarios. We observe that the disclosure strategy may be independent of the latent feature in some scenarios, for which explicit expressions for the performance are provided.
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