PREEN: An Aggregation Mechanism for Privacy-Preserving Smart-Metering Communications
Tao Wang, Jing Jin, Xin Gu, Yuping Zhang, Xiaoyu Chen
Abstract
The widespread use of home consumer eletronics (CEs) provides residents facility, assistance and comfort. Meanwhile, smart meters are deployed at households to optimize electricity supply and demand and improve electricity efficiency. These meters monitor, collect and aggregate the electricity consumption of the home CEs in a near-real-time way. However, such processes seriously invade consumer privacy, e.g., household occupancy and economic status. Many existing privacy-preserving schemes rely on the assumption that certain components can be trusted, which is hard to be ensured, and cannot resist against collusion attacks. In this study, we leverage differential privacy and a re-encryption model to produce a privacy-preserving aggregation mechanism, called PREEN, for smart-metering communications. PREEN simultaneously facilitates data aggregation, differential privacy, and protection against various attacks. Specifically, we adopt ElGamal algorithm to construct the re-encryption scheme and differential privacy to aggregate the consumption measurements of residential users at the gateways and control center, thereby enabling resistance against slashing privacy attacks. Strict and detailed security proofs as well as extensive performance evaluations are adopted to demonstrate that the proposed PREEN provides rigorous privacy preservation and confidentiality against diverse external and internal attacks, while remaining a comparable or even lower computational overhead and a negligible communication overhead and a reasonable accuracy in terms of the utility of differential privacy, compared to the state-of-the-art mechanisms.