Paper 2013/743

Privacy Preserving Unique Statistics in a Smart Grid

Iraklis Leontiadis, Melek Önen, and Refik Molva

Abstract

Smart meters are widely deployed to provide fine-grained data that correspond to tenant power consumption. These data are analyzed by suppliers for personalized billing, more accurate statistics and energy consumption predictions. 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 unique 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.