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Smart Meter Privacy Based on Adversarial Hypothesis Testing
KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2276-2079
KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-0036-9049
Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom.
2017 (English)In: Proceedings of the IEEE International Symposium on Information Theory (ISIT) 2017, IEEE, 2017, 774-778 p.Conference paper, Published paper (Refereed)
Abstract [en]

Privacy-preserving energy management is studied in the presence of a renewable energy source. It is assumed that the energy demand/supply from the energy provider is tracked by a smart meter. The resulting privacy leakage is measured through the probabilities of error in a binary hypothesis test, which tries to detect the consumer behavior based on the meter readings. An optimal privacy-preserving energy management policy maximizes the minimal Type II probability of error subject to a constraint on the Type I probability of error. When the privacy-preserving energy management policy is based on all the available information of energy demands, energy supplies, and hypothesis, the asymptotic exponential decay rate of the maximum minimal Type II probability of error is characterized by a divergence rate expression. Two special privacy-preserving energy management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are then considered and their performances are compared. Further, it is shown that the energy supply alphabet can be constrained to the energy demand alphabet without loss of optimality for the evaluation of a single-letter-divergence privacy-preserving guarantee.

Place, publisher, year, edition, pages
IEEE, 2017. 774-778 p.
National Category
Communication Systems Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-205209OAI: oai:DiVA.org:kth-205209DiVA: diva2:1087746
Conference
IEEE International Symposium on Information Theory (ISIT) 2017, Aachen, Germany, Jun. 25-30, 2017
Note

QC 20170821

Available from: 2017-04-10 Created: 2017-04-10 Last updated: 2017-08-21Bibliographically approved

Open Access in DiVA

fulltext(209 kB)3 downloads
File information
File name FULLTEXT01.pdfFile size 209 kBChecksum SHA-512
40fe9eb5ddb2931f6badfab19cc6e01e9517aa8f962b5f8e5a3f3c0e05fbcd0c02ee5fd2be95d6ea996cfd9eb737c6ac7ee597579d62dc967db663495b76ac9e
Type fulltextMimetype application/pdf

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https://isit2017.org/

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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