An efficient data aggregation scheme with local differential privacy in smart grid

Gai, Na; Xue, Kaiping; Zhu, Bin; Yang, Jiayu; Liu, Jianqing; He, Debiao

doi:10.1016/j.dcan.2022.01.004

Cited by 33 publications

(16 citation statements)

References 25 publications

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“…Through normalizing and modifying the confidence score vectors with a differential privacy mechanism, Ye et al [53] proposed a one-parameter defense method to against both model inversion and membership inference attacks. Based on randomized responses, Gai et al [51] presented an efficient data aggregation scheme satisfying local differential privacy with privacy-preserving for smart grids. However, they only achieve differential privacy for one-dimensional data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids

Zhao,

Xu,

Han

et al. 2024

IEEE Internet Things J.

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The smart grid (SG) is a new type of grid that integrates traditional power grid with the Internet of Things (IoT) to make the entire grid system more compatible, controllable and self-healing. However, the flourishing of SG still faces some challenges in term of privacy-preserving data aggregation. Previous multi-dimensional data aggregation schemes need heavy computation operations, cannot support multi-subset data aggregation, and resist neither collusion attack among the gateway (GW) and control center (CC) nor differential attack. To solve these issues, we propose a privacy-preserving data aggregation scheme for fog-based smart grids to achieve multi-dimensional and multi-subset data aggregation. The parallel composability of differential privacy is used to reasonably allocate the privacy budget, which can provide higher data utility in multi-dimensional data aggregation. In addition, each user's multi-dimensional power consumption data will be structured as a composite data by utilizing Chinese Remainder Theorem (CRT), which will further reduce the computational overhead. Security analysis shows that our scheme can resist differential attack, eavesdropping attack, collusion attack and active attack. Evaluation of the performance also demonstrates that our scheme is more efficient in terms of computational overhead and communication overhead.

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Section: Related Workmentioning

confidence: 99%

“…In this section, we compare our proposed scheme with [13], [17], [24], [25], [31], [51]- [56] from functions, computational and communication overheads, and errors in the fog-based smart grid system.…”

Section: Performance Evaluationmentioning

confidence: 99%

PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids

Zhao,

Xu,

Han

et al. 2024

IEEE Internet Things J.

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show abstract

“…Gai et al. proposed a data aggregation scheme with local differential privacy (LDP) in smart grids [ 33 ] by discretizing and aggregating these data to meet the privacy guarantees of LDP and finally estimating the total or average power consumption after combining randomized responses.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Sun

Cardell-Oliver

et al. 2023

Sensors

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Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy.

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“…Local differential privacy (LDP) is proposed as a distributed variant of differential privacy, which locally perturbs the data of each user on the client-side [31][32][33]. It also inherits the comprehensive characteristics of centralized differential privacy.…”

Section: The Hybrid Poi Recommendation Model Based On Ldpmentioning

confidence: 99%

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

Mei

et al. 2022

Complex Intell. Syst.

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With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k-RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.

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An efficient data aggregation scheme with local differential privacy in smart grid

Cited by 33 publications

References 25 publications

PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids

PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

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