Privacy-preserving data aggregation scheme against internal attackers in smart grids

Abstract

With fast advancements of communication, systems and information technologies, a smart grid (SG) could bring much convenience to users because it could provide a reliable and efficient energy service. The data aggregation (DA) scheme for the SG plays an important role in evaluating information about current energy usage. To achieve the goal of preserving users' privacy, many DA schemes for the SG have been proposed in last decade. However, how to withstand attacks of internal adversaries is not considered in those schemes. To enhance preservation of privacy, Fan et al. proposed a DA scheme for the SG against internal adversaries. In Fan et al.'s DA scheme, blinding factors are used in evaluating information about current energy usage and the aggregator cannot get the consumption information of any individual user. Fan et al. demonstrated that their scheme was secure against various attacks. However, we find that their scheme suffers from the key leakage problem, i.e., the adversary could extract the user's private key through the public information. To overcome such serious weakness, this paper proposes an efficient and privacy-preserving DA scheme for the SG against internal attacks. Analysis shows that the proposed DA scheme not only overcome the key leakage problem in Fan et al.'s DA scheme, but also has better performance.