4.7 Article

An efficient and privacy-preserving blockchain-based secure data aggregation in smart grids

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ELSEVIER
DOI: 10.1016/j.seta.2023.103414

Keywords

Smart grid; Privacy preserving; Integrity; Authentication; Distributed ledger technology

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Smart Grids have several advantages over traditional grids, but they also introduce security and privacy issues. Data aggregation plays a crucial role in protecting user consumption data, but existing schemes have limitations. This paper proposes a decentralized secure data aggregation scheme using blockchain to preserve the privacy, integrity, and authentication of individual consumption data.
Smart Grids (SGs) present a number of advantages over traditional grid like reduce energy cost, reduce energy wastage, and increase reliability and transparency. However, it also introduces a number of security and privacy issues not foreseen before. These are very serious issues from consumer's perspective who do not want any malicious entity to infer any personal information from the consumption data. Data aggregation plays a significant role in protecting user's consumption data. SGs deploy a single entity, called an aggregator, to collect and aggregate end users' encrypted consumption data. While a number of secure data aggregation schemes have been proposed, many existing schemes suffer from a single point of failure in the aggregation process. Recently, some blockchain-based data aggregation schemes have been proposed to overcome this problem. While overcoming the failure issues, these schemes do not provide the necessary security and privacy requirements of smart grids. Furthermore, these schemes suffer from high computation and communication cost due to the use of RSA-based signatures. In order to solve these issues, this paper proposes a decentralized secure data aggregation scheme using blockchain which preserves privacy, integrity, authentication, and confidentiality of individual consumption data. Experimental results show that the proposed scheme effectively protects end user consumption data.

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