A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility

• URL: http://arxiv.org/abs/2508.14744v1
• Date: Wed, 20 Aug 2025 14:40:33 GMT
• Title: A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility
• Authors: Farid Zaredar, Morteza Amini,
• Abstract summary: We propose a collusion-resistant, privacy-preserving aggregation protocol for smart metering in operational services.<n>Our scheme aggregates perturbed readings using the additive homomorphic property of the Paillier cryptosystem.<n>We evaluate the protocol in terms of both performance and privacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern grids have adopted advanced metering infrastructure (AMI) to facilitate bidirectional communication between smart meters and control centers. This enables smart meters to report consumption values at predefined intervals to utility providers for purposes including demand balancing, load forecasting, dynamic billing, and operational efficiency. Compared to traditional power grids, smart grids offer advantages such as enhanced reliability, improved energy efficiency, and increased security. However, utility providers can compromise user privacy by analyzing fine-grained readings and extracting individuals' daily activities from this time-series data. To address this concern, we propose a collusion-resistant, privacy-preserving aggregation protocol for smart metering in operational services. Our protocol ensures privacy by leveraging techniques such as partially additive homomorphic encryption, aggregation, data perturbation, and data minimization. The scheme aggregates perturbed readings using the additive homomorphic property of the Paillier cryptosystem to provide results for multiple operational purposes. We evaluate the protocol in terms of both performance and privacy. Computational, memory, and communication overhead were examined. The total execution time with 1024-bit key size is about 2.21 seconds. We also evaluated privacy through the normalized conditional entropy (NCE) metric. Higher NCE values, closer to 1, indicate stronger privacy. By increasing noise scale, the NCE value rises, showing perturbed values retain minimal information about the original, thereby reducing risks. Overall, evaluation demonstrates the protocol's efficiency while employing various privacy-preserving techniques.

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