Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering

Ni, Jianbing; Zhang, Kuan; Alharbi, Khalid; Lin, Xiaodong; Zhang, Ning; Shen, Xuemin

doi:10.1109/tsg.2017.2673843

Cited by 83 publications

(34 citation statements)

References 25 publications

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“…To provide personalized recommendation in big data resulting from social networks and maintain user privacy, a cloud-assisted differentially private video recommendation system based on distributed online learning was proposed [11]. The work in [12] proposed a new privacy preserving smart metering scheme for smart grid, which supports data aggregation, differential privacy, fault tolerance, and rangebased filtering simultaneously. To et al [13] introduced a novel privacy-aware framework for spatial crowdsourcing, which enables the participation of workers without compromising their location privacy.…”

Section: Related Workmentioning

confidence: 99%

“…Differential privacy (DP), a privacy preserving model originated from statistical database, has currently drawn considerable attentions in research communities [10][11][12][13][14][15][16][17] due to (i) its rigorous and provable privacy guarantee and (ii) its assumption of adversaries' arbitrary background knowledge. However, DP actually assumes that the tuples in databases are independent [18].…”

Section: Introductionmentioning

confidence: 99%

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An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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“…In [177], Ni et al proposed EIGamal encryption based scheme for smart meters. The scheme not only aggregates the consumption data from SMs but also defends fault tolerance of malfunctioning SMs.…”

Section: Privacy/confidentiality Threats Countermeasures In Sg Metmentioning

confidence: 99%

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Kumar

Lin

Bai

et al. 2019

IEEE Commun. Surv. Tutorials

250

115

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Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring and managing multiple domains in the SG. Despite the salient features of SMI, security and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: (i) threats in system-level security, (ii) threats and/or theft of services, and (iii) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in smart grid metering networks.

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“…In this section, an overview of existing fog-enabled privacy preserving-data aggregation (e.g., [9], [10], [12]- [14] and traditional privacy-preserving data aggregation (e.g., [17][18][19][20][21][22][23][24][25][26][27][28]) schemes in SG is provided in detail. Existing fog-enabled data aggregation schemes in SG, such as those presented in [9], [10], have a number of limitations.…”

Section: A Related Workmentioning

confidence: 99%

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

Saleem

Khan

Malik

et al. 2020

IEEE Internet Things J.

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With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

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Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering

Cited by 83 publications

References 25 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

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