智能电网中轻量级细粒度的多维多子集隐私保护数据聚合

doi:10.3969/j.issn.1671-1122.2025.05.007

摘要/Abstract

摘要：

智能电网在利用智能电表进行数据采集与传输时，存在用户隐私泄露风险，同时还面临着缺乏细粒度数据分析、计算开销高及系统稳定性差等挑战。针对这些问题，文章提出一个智能电网中轻量级细粒度的多维多子集隐私保护数据聚合（LFMMP-DA）方案。首先，通过高级加密标准（AES）算法保证数据安全传输；然后，利用多秘密共享和超递增序列实现细粒度的多维多子集聚合，该方法不仅使管理中心能够获取不同维度的多子集数据和用户数量，还确保即使智能电表和雾节点同时发生故障，系统仍能正常执行数据聚合和解密；最后，通过集成雾计算、区块链和星际文件系统，设计了一个无需可信第三方的分布式存储的数据聚合模型。安全分析与实验结果表明，该方案能够有效保障用户隐私和数据完整性，并在计算开销和通信开销方面最高可分别降低94.94%和80.00%，验证了所提方案的有效性。

关键词: 容错, 隐私保护, 数据聚合, 智能电网, 区块链

Abstract:

In the smart grid, when using smart meters for data collection and transmission, there is a risk of user privacy leakage. Additionally, it faces challenges such as insufficient fine-grained data analysis, high computational overhead, and poor system stability. To address these challenges, a lightweight fine-grained multi-dimensional multi-subset privacy-preserving data aggregation(LFMMP-DA) scheme was proposed. Firstly, the advanced encryption standard (AES) algorithm was used to ensure secure data transmission. Secondly, multi-secret sharing and super-increasing sequences were utilized to achieve fine-grained multi-dimensional and multi-subset aggregation. This method not only enabled the management center to obtain multi-subset data and users’ counts across different dimensions but also ensured that the system could still perform data aggregation and decryption normally, even if smart meters and fog nodes fail simultaneously. Finally, by integrating fog computing, blockchain and interplanetary file system, a data aggregation model with distributed storage was designed. The security analysis and experimental results show that the LFMMP-DA scheme can effectively protect user privacy and data integrity, reducing computational and communication overheads by up to 94.94% and 80.00%, respectively, thus validating the effectiveness of the proposed scheme.

Key words: fault tolerance, privacy preservation, data aggregation, smart grid, blockchain

中图分类号:

TP309

秦金磊, 康毅敏, 李整. 智能电网中轻量级细粒度的多维多子集隐私保护数据聚合[J]. 信息网络安全, 2025, 25(5): 747-757.

QIN Jinlei, KANG Yimin, LI Zheng. Lightweight Fine-Grained Multi-Dimensional Multi-Subset Privacy-Preserving Data Aggregation for Smart Grid[J]. Netinfo Security, 2025, 25(5): 747-757.

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符号	定义
$N$	加密数据的模数
$H{{H}_{1}}$	安全哈希函数
$p$	多秘密共享的模数
$q$	群的阶数
o	安全参数
$G$	阶为$q$的椭圆曲线群
$\overrightarrow{a}$	超递增序列
$[{{r}_{k}},{{r}_{k+1}})$	第$k$个子集
$X$	所有维度数据最大值
$t$	多秘密共享的阈值
$m$	FN总数
$n$	SM总数
$\omega $	区域个数
$s{{k}_{F{{N}_{j}}-S{{M}_{i}}}}$	$F{{N}_{j}}$和$S{{M}_{i}}$的对称密钥
$s{{k}_{MC-S{{M}_{i}}}}$	MC和$S{{M}_{i}}$的对称密钥
$s{{k}_{MC-F{{N}_{j}}}}$	MC和$F{{N}_{j}}$的对称密钥
$I{{D}_{S{{M}_{i}}}}$	$S{{M}_{i}}$身份标识
$I{{D}_{F{{N}_{j}}}}$	$F{{N}_{j}}$身份标识
$s{{k}_{i}}$	$S{{M}_{i}}$私钥
$p{{k}_{i}}$	$S{{M}_{i}}$公钥
$s{{k}_{j}}$	$F{{N}_{j}}$私钥
$p{{k}_{j}}$	$F{{N}_{j}}$公钥
${{t}_{s}}$	时间戳
${{D}_{i}}$	$l$维数据向量
${{\sigma }_{i}}$	$S{{M}_{i}}$签名
${{\sigma }_{j}}$	$F{{N}_{j}}$签名

功能特性	文献[3]	文献[8]	文献[10]	文献[11]	文献[12]	文献[15]	文献[21]	LFMMP-DA
隐私性	√	√	√	√	√	√	√	√
完整性	√	√	√	√	√	√	√	√
轻量级	×	×	×	×	×	×	×	√
SM容错	√	×	√	√	×	√	×	√
FN容错	×	×	×	×	×	×	×	√
抗合谋	√	√	×	√	√	√	×	√
多维多子集	×	×	√	√	√	×	×	√
无可信第三方	×	√	×	×	×	√	√	√
分布式存储	×	×	×	×	×	×	√	√

符号	操作描述	时间/ms
${{T}_{pm}}$	点乘	12.1926
${{T}_{bp}}$	双线性配对	6.9771
${{T}_{AES-Enc}}$	AES-128 加密	2.1613
${{T}_{AES-Dec}}$	AES-128 解密	0.2535
${{T}_{Lag}}$	拉格朗日插值	0.1445
${{T}_{me}}$	模幂	2.5767
${{T}_{hp}}$	哈希到点	26.8531

方案	SM计算开销/ms	FN计算开销/ms	MC计算开销/ms
文献[10]	lT_pm+T_me+T_hp= 29.4298+12.1926l	(n+1)T_hp= 26.8531+26.8531n	T_hp+T_me= 29.4298
文献[11]	3T_pm+T_me+T_hp= 66.0076	(n+1)T_bp+3T_pm+ T_me+T_hp=72.9847+ 6.9771n	2T_bp+T_me= 16.5309
文献[12]	(w+8)T_pm+ (3w+6)T_hp= 258.6594+ 92.7519w	(2n+1)T_pm+(n+1)T_hp= 39.0457+51.2383n	(2n+3)T_pm+ (2n+1)T_bp= 43.5549+38.3394n
LFMMP-DA	T_AES_-_Enc+T_pm+ T_hp=41.2070	(n+1)T_bp+T_AES_-_Dec+ T_AES_-_Enc+T_pm+T_hp= 48.4376+6.9771n	(m+1)T_bp+T_AES_-_Dec= 7.2306+6.9771m

方案	$l$
方案	4	8	12	16	20
文献[10]	78.2002ms	126.9706ms	175.7410ms	224.5114ms	273.2818ms
文献[11]	66.0076 ms	66.0076 ms	66.0076 ms	66.0076 ms	66.0076 ms
文献[12]	815.1708 ms	815.1708 ms	815.1708 ms	815.1708 ms	815.1708 ms
LFMMP-DA	41.2070 ms	41.2070 ms	41.2070 ms	41.2070 ms	41.2070 ms