Fog-Based Conditional Privacy-Preserving Data Batch Verification in Smart Grid

Abstract

The smart grid is already widespread for the purpose of managing energy generation and distribution. In this system, frequent interactions between devices generate mass data, which requires robust processing capability. Fog computing has the advantage of latency and accessibility that can be applied in smart grids for improving data throughput and energy management efficiency to make the system sustainable. In addition, smart meters (SMs) are responsible for collecting real-time power consumption reports, and then sending them to the service provider (SP). The SP adjusts the power distribution strategy and develops an energy management optimization plan. Unfortunately, this process can reveal sensitive information that the user does not want to disclose. Therefore, we propose a conditional privacy-preserving data aggregation with batch verification scheme. Firstly, as an aggregator, fog devices aggregate masses of data from users into one with simple operation, which reduces communication complexity. Meanwhile, it supports batch verification on the SP side. Secondly, the scheme not only avoids certificates management and key escrow, but implements encrypting and signing in a logical step, thus satisfying confidentiality, integrity and authentication. Finally, our scheme provides conditional privacy-preserving, in which messages can be authenticated anonymously and malicious messages can be traced. Extensive performance evaluation details our scheme is efficient with low computation complexity and communication overheads.