Fog-enabled secure multiparty computation based aggregation scheme in smart grid. (September 2021)

• Record Type: Journal Article
• Authors: Khan, Hayat Mohammad; Khan, Abid; Jabeen, Farhana; Anjum, Adeel; Jeon, Gwanggil
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Fog computing -- Smart grid -- Privacy preservation -- Multi-party computation -- Homomorphic encryption -- Secure aggregation -- Advanced metering infrastructure -- Fault-Tolerance -- Metering data -- Elliptic curve digital signature scheme; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107358
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The Smart Grid (SG) is a modern power grid infrastructure for improved efficiency and reliability using modern telecommunications infrastructure, sensing, and measuring technologies. It provides sustainable electricity to customers and enables them to monitor their electricity use. The Fog Nodes (FNs) are deployed between the Cloud Control Center (CCC) and Smart Meters (SMs) to improve efficiency and reliability. The CCC is collecting usage data through FNs at regular intervals from SMs. As SM data contains private customer data, a secure scheme is required to ensure customer privacy during data collection. The usage view behavior of consumers is only possible if internal or external malicious entities collude together. In this paper, we present the Fog-Enabled Secure Multiparty Computation (FESMPC) based aggregation scheme for SG. This paper's key contributions to the literature on the topic of secure privacy-aware aggregation schemes in SGs are threefold. Firstly, a secure aggregation scheme that preserves data privacy and ensures fault-tolerance is presented. The scheme is robust against the attacks of collusive and False Data Injection (FDI). A collusive attack is managed through enhanced Shamir's secret scheme. At the same time, privacy during data aggregation is achieved through a Paillier homomorphic encryption scheme. Secondly, detailed security analyses are provided to show that the proposed scheme supports data privacy, source authentication, fault tolerance, and prevents FDI attacks. Thirdly, the paper presents extensive performance evaluations demonstrating that the proposed program scheme is efficient in encryption, aggregation, decryption, and less communication costs than the three existing state-of-the-art programs.