Achieving differential privacy of data disclosure in the smart grid

Zhao, Jing; Jung, Taeho; Yu, Wenbin; Li, Xiangyang

doi:10.1109/infocom.2014.6847974

Cited by 125 publications

(65 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [27] proposed a metric for load signature moderation schemes, while Eibl et al [28] examined the effect of adding Laplacian noise to aggregated smart meter load profiles. Shankar et al [29] use the F-Test to measure and compare raw and noisy load profiles.…”

Section: Privacy Metricsmentioning

confidence: 99%

Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering

Buescher

Boukoros

Bauregger

et al. 2017

Proceedings on Privacy Enhancing Technologies

View full text Add to dashboard Cite

Abstract:The widespread deployment of smart meters that frequently report energy consumption information, is a known threat to consumers' privacy. Many promising privacy protection mechanisms based on secure aggregation schemes have been proposed. Even though these schemes are cryptographically secure, the energy provider has access to the plaintext aggregated power consumption. A privacy trade-off exists between the size of the aggregation scheme and the personal data that might be leaked, where smaller aggregation sizes leak more personal data. Recently, a UK industrial body has studied this privacy trade-off and identified that two smart meters forming an aggregate, are sufficient to achieve privacy. In this work, we challenge this study and investigate which aggregation sizes are sufficient to achieve privacy in the smart grid. Therefore, we propose a flexible, yet formal privacy metric using a cryptographic game based definition. Studying publiclyavailable, real world energy consumption datasets with various temporal resolutions, ranging from minutes to hourly intervals, we show that a typical household can be identified with very high probability. For example, we observe a 50% advantage over random guessing in identifying households for an aggregation size of 20 households with a 15-minutes reporting interval. Furthermore, our results indicate that single appliances can be identified with significant probability in aggregation sizes up to 10 households.

show abstract

Section: Privacy Metricsmentioning

confidence: 99%

Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering

Buescher

Boukoros

Bauregger

et al. 2017

Proceedings on Privacy Enhancing Technologies

View full text Add to dashboard Cite

show abstract

“…Counteracting attacks based on Non-Intrusive Load Monitoring (NILM) of energy usage traces has been addressed by a consistent body of literature (see [16] for a survey). Typical solutions rely on battery-based load hiding [17], [18], on noise injection (e.g. according to the framework of differential privacy [19]) or on multi-party computation cryptographic techniques [20], [21].…”

Section: Related Workmentioning

confidence: 99%

Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

Rottondi¹,

Barbato²,

Chen³

et al. 2017

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Abstract-Demand Side Management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits. This paper proposes a distributed Privacy-Friendly DSM system that preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN). We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern. The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented.

show abstract

“…They are presented to achieve collusion-resistant or fault-tolerable data aggregation with privacy preservation. However, they leverage the differential privacy [18], [19], [20], [21] in various ways to achieve privacy as well as collusion (or fault) tolerance. The results given by their solutions contain asymptotically bounded error, and those works may not be applicable when exact results are desired.…”

Section: Related Workmentioning

confidence: 99%

Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel

Jung

Wan

2015

IEEE Trans. Dependable and Secure Comput.

Self Cite

View full text Add to dashboard Cite

Abstract-Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants' privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We first propose several protocols that successfully guarantee data privacy under semi-honest model, and then present advanced protocols which tolerate up to k passive adversaries who do not try to tamper the computation. Under this weak assumption, we limit both the communication and computation complexity of each participant to a small constant. At the end, we present applications which solve several interesting problems via our protocols.

show abstract

Achieving differential privacy of data disclosure in the smart grid

Cited by 125 publications

References 26 publications

Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering

Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering

Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

Collusion-Tolerable Privacy-Preserving Sum and Product Calculation without Secure Channel

Contact Info

Product

Resources

About