Lightweight Fine-Grained Multi-Dimensional Multi-Subset Privacy-Preserving Data Aggregation for Smart Grid

doi:10.3969/j.issn.1671-1122.2025.05.007

Abstract

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

CLC Number:

TP309

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.

Figures/Tables 15

References 27

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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