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Privacy-preserving quantization learning with applications to smart meters

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Abstract

Consider a source coding problem in presence of two dependent with memory sources (X, Y), for which only X is available at the encoder (referred to Alice). We first study the design of vector quantization for the situation where one of the source outputs, i.e., X, must be transmitted to the receiver (referred to Bob) within a prescribed distortion tolerance as in ordinary source coding. On the other hand, the other source, i.e., Y, has to be kept as secret as possible from the receiver or wiretappers. We next consider the opposite case where Y represents a relevant utility sequence to be reconstructed at Bob while trying to keep information about X secret from an eventual eavesdropper. A practical application involving electric consumption data measured from real houses is finally investigated.
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Dates and versions

hal-01699394 , version 1 (02-02-2018)

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Maggie Mhanna, Pablo Piantanida, Pierre Duhamel. Privacy-preserving quantization learning with applications to smart meters. IEEE International Conference on Communications (ICC 2017), May 2017, Paris, France. ⟨10.1109/ICC.2017.7997332⟩. ⟨hal-01699394⟩
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