面向智能电网的高效隐私保护多维多子集数据聚合方案

An efficient privacy-preserving multi-dimensional and multi-subset data aggregation scheme for smart grids

摘要

在智能电网中,隐私保护数据聚合技术被用于在不泄露用户用电数据的前提下实现数据统计分析。然而,现有方案大多仅支持单维数据聚合,统计功能相对有限,难以支持指定用电区间内的用户数量统计,同时在一定程度上依赖可信第三方,且在效率方面仍有提升空间。针对上述问题,本文基于Paillier同态加密、中国剩余定理与超增序列,提出了一个支持多维数据聚合与多子集统计的方案。该方案可实现用电数据的总和、均值与方差计算,并支持指定用电区间内用户数量统计,同时避免可信实体依赖并降低计算复杂度。安全性分析表明,方案满足密文的不可区分性与签名的不可伪造性,能够抵御窃听攻击与共谋攻击。性能分析表明,该方案在计算与通信开销方面具有较高效率。

Abstract

In smart grids,privacy-preserving data aggregation techniques are used to enable statistical analysis of data without disclosing users' electricity consumption data.However,most existing schemes support only single-dimensional data aggregation and offer relatively limited statistical functions,making it difficult to count the number of users within a specified electricity consumption interval.Moreover,they rely to some extent on a trusted third party and still have room for improvement in terms of efficiency.To address the above issues,this paper proposes a scheme that supports multi-dimensional data aggregation and multi-subset statistics based on Paillier homomorphic encryption,the Chinese Remainder Theorem,and super-increasing sequences.The proposed scheme enables the calculation of the sum,mean,and variance of electricity consumption data,as well as the counting of users within a specified consumption interval,while eliminating the reliance on a trusted entity and reducing computational complexity.Security analysis shows that the scheme achieves ciphertext indistinguishability and signature unforgeability,and is resistant to eavesdropping and collusion attacks.Performance evaluation results demonstrate that the scheme offers high efficiency in terms of computational and communication overhead.

关键词

隐私保护 / 数据聚合 / 多维 / 多子集 / 智能电网

Key words

privacy-preserving / data aggregation / multi-dimensional / multi-subset / smart grid

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基金资助

国家自然科学基金项目(62462014);贵州省高层次创新人才项目(黔科合平台人才-GCC[2022]018-1);贵州省教育厅2023年自然科学研究项目(黔教技[2023]010号);贵州省科技计划重点项目(黔科合基础-ZK[2024]重点058号)