Privacy-preserving quantization learning with applications to smart meters | IEEE Conference Publication | IEEE Xplore

Privacy-preserving quantization learning with applications to smart meters


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...Show More

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.
Date of Conference: 21-25 May 2017
Date Added to IEEE Xplore: 31 July 2017
ISBN Information:
Electronic ISSN: 1938-1883
Conference Location: Paris, France

References

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