EPPSQ: Achieving efficient and privacy-preserving statistics queries over encrypted data in smart grids

Abstract

Outsourcing encrypted data and query services to clouds have been widely adopted by data owners, such as in the energy, healthcare, and transportation sectors, usually for economic considerations. However, this is usually prone to data privacy breaches. In smart grids, although many privacy-preserving data query solutions have been presented in the literature, they either suffer from low query efficiencies or limited capabilities of statistics queries (say the aggregation, variance, or extrema only). To mitigate this gap, in this paper we propose an efficient and privacy-preserving statistics query scheme over encrypted data in smart grids, coined EPPSQ, which can achieve diverse statistics queries in an efficient and privacy-preserving way. Specifically, we first develop a public key encryption based secure data collection protocol for data owners. Then, we design a two-server model based privacy-preserving statistics query protocol for data requesters. In addition, four algorithms respectively for securely computing the statistics (i.e., max/min, variance, mean, and count) over the encrypted data in smart grids are crafted. Security analysis shows that the proposed EPPSQ scheme can effectively guarantee data privacy while outsourcing the encrypted data and query services in smart grids. Further, extensive experiments demonstrate that the proposed EPPSQ scheme outperforms existing CKKS-based or BFV-based studies in terms of computational efficiency.