Privacy-Preserving Energy Theft Detection in Smart Grids: A P2P Computing Approach

Abstract

In the U.S., energy theft causes about six billion dollar losses to utility companies (UCs) every year. With the grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the smart meters used in grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require users to send their private information, e.g., load profiles or meter readings at certain times, to the UCs, which invades users' privacy and raises serious concerns about privacy, safety, etc. To the best of our knowledge, we are the first to investigate the energy theft detection problem considering users' privacy issues. Specifically, in this paper, utilizing peer-to-peer (P2P) computing, we propose three distributed algorithms to solve a linear system of equations (LSE) for users' honesty coefficients. Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.