A smart grid data privacy-preserving aggregation approach with authentication

Abstract

Authentication of smart grid privacy-preserving aggregation addresses two of the key privacy and security issues of the smart grids: user data confidentiality and grid node communication safety. The proposed study elaborates on a new approach to data aggregation with authentication in smart grid systems for the safe and efficient exchange of information. The proposed solution would apply techniques, such as homomorphic encryption along with advanced cryptographic techniques, to calculate encrypted data without leaking sensitive information. Data and device integrity are more likely to be maintained when using better authentication techniques like blockchain and quantum key distribution (QKD). This dual layered aggregation with privacy-preserving combined with robust authentication can strengthen the smart grids against unauthorized access and data tampering, along with other cyber-attacks. The results show that the proposed approach for aggregation in smart meters is more accurate and useful in terms of data as compared to the conventional approaches. As far as mean relative error (MRE) is concerned, the MRE of the proposed layer model is 0.0007, which is substantially smaller than the differentially private model (0.0023) and Gaussian model (0.0058). The minimum MRE of the proposed model was achieved in the aggregator layer at 0.0029 compared with the corresponding differentially- private model’s 0.0063 and Gaussian model’s 0.0117. As the privacy parameter ε increases, noise levels drop precipitously from 14.137738 for ε = 0.1 to 0.282786 for ε = 5.0. The proposed methodology improves smart grid data aggregation with a balance between privacy and accuracy.