DASEC 2014, 1st International Workshop for Big Analytics for Security, in conjunction with ICDCS, 30 June-3 July 2014, Madrid, Spain
Smart meters are widely deployed to provide ﬁne-grained information that correspond to tenant power consumption. These data are analyzed by suppliers for more accurate statistics, energy consumption predictions and personalized billing. Indirectly this aggregation of data can reveal personal information of tenants such as number of persons in a house, vacation periods and appliance preferences. To date, work in the area has focused mainly on privacy preserving aggregate statistical functions as the computation of sum. In this paper we propose a novel solution for privacy preserving individual data collection per smart meter. We consider the operation of identifying the maximum consumption of a smart meter as an interesting property for energy suppliers, as it can be employed for energy forecasting to allocate in advance electricity. In our solution we employ an order preserving encryption scheme in which the order of numerical data is preserved in the ciphertext space. We enhance the accuracy of maximum consumption by utilizing a delta encoding scheme.
© 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.