Lightweight Privacy-Friendly Aggregation Scheme Against Internal Attacks for Smart Grids

While real-time electricity consumption data of users in smart grids can enable value-added services, such as Big Data analytics, each individual user's privacy needs to be protected. How to balance data utility and privacy protection is a significant issue, of which privacy-preserving data aggregation (PPDA) is a viable solution. Prior to this, researchers have proposed a number of PPDA schemes to address the above challenge. Unluckily, most of them suffer from security and privacy drawbacks, while others are inappropriate for resource-limited smart meters due to high cost of cryptographic operations. To tackle this issue, in this article, we propose a pairing-free and exponentiation-free certificateless PPDA scheme named CL-PPDA for smart grids. We prove the security of our design and analyze its performance. Comparative analyses with state-of-the-art work in theory and experiment show that our design not only has better security properties, but also has competitive computation overhead, especially on the resource-constrained smart meter side. Besides, we present an extension of our CL-PPDA scheme to support multidimensional data aggregation, which further enriches the functionality of our design.