Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption


In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.


Computer Science


This work was supported by Japan-US Network Opportunity 2 by Commissioned Research of the National Institute of Information and Communications Technology (NICT), Japan, and NSF grants SATC-2030611, SATC-2030624, DGE-1433659, CNS-1818942.

Keywords and Phrases

Bilinear Pairing; Elliptic Curve Cryptography; Homomorphic Encryption; Smart Grids

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Document Type

Article - Conference proceedings

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Publication Date

01 Jan 2022