G. Kalogridis, Costas Efthymiou, S. Denic, T. Lewis, R. Cepeda
{"title":"Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures","authors":"G. Kalogridis, Costas Efthymiou, S. Denic, T. Lewis, R. Cepeda","doi":"10.1109/SMARTGRID.2010.5622047","DOIUrl":null,"url":null,"abstract":"Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the home's load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"443","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 First IEEE International Conference on Smart Grid Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMARTGRID.2010.5622047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 443
Abstract
Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the home's load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.