LPPMM-DA: Lightweight Privacy-Preserving Multi-Dimensional and Multi-Subset Data Aggregation for Smart Grid

Abstract

The smart grid facilitates data centers in collecting real-time power consumption data from users, which is essential for effective power management. Such real-time data may inadvertently disclose the identities and activities of power users. Data aggregation has been identified as a viable solution to this challenge, enabling data centers to obtain only the aggregate power consumption data without accessing individual user information. However, most existing aggregation methodologies are limited to multi-dimensional data aggregation and fail to ensure user privacy, data integrity, and authentication. In this study, we propose a ring signature based multi-dimensional and multi-subset aggregation (LPPMM-DA) scheme. This proposed method allows the data center to compute both the total power consumption and the number of users within each subset across various dimensions. Based on the hardness assumption of the Elliptic Curve Discrete Logarithm Problem (ECDLP), the ring signature utilized in our scheme is demonstrably unforgeable against adaptive chosen message attacks within the random oracle model. A comprehensive analysis indicates that the proposed scheme meets the security requirements for data aggregation in the smart grid context. Furthermore, performance evaluations reveal that the implementation of this scheme results in lower computational and communication overhead compared to existing related approaches.