Privacy-Preserving Quantization Learning for Distributed Detection with Applications to Smart Meters

Abstract : This thesis investigates source coding problems in which some secrecy should be ensured with respect to eavesdroppers. In the first part, we provide some new fundamental results on both detection and secrecy oriented source coding in the presence of side information at the receiving terminals. We provide several new results of optimality and single-letter characterization of the achievable rate-error-equivocation region, and propose practical algorithms to obtain solutions that are as close as possible to the optimal, which requires the design of optimal quantization in the presence of an eavesdropper In the second part, we study the problem of secure estimation in a utility-privacy framework where the user is either looking to extract relevant aspects of complex data or hide them from a potential eavesdropper. The objective is mainly centered on the development of a general framework that combines information theory with communication theory, aiming to provide a novel and powerful tool for security in Smart Grids. From a theoretical perspective, this research was able to quantify fundamental limits and thus the tradeoff between security and performance (estimation/detection).
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  • HAL Id : tel-01691554, version 1

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Maggie Mhanna. Privacy-Preserving Quantization Learning for Distributed Detection with Applications to Smart Meters. Signal and Image Processing. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLS047⟩. ⟨tel-01691554⟩

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