article

Differential privacy for real smart metering data

Authors:

Dominik EngelAuthors Info & Claims

Computer Science - Research and Development, Volume 32, Issue 1-2

Pages 173 - 182

https://doi.org/10.1007/s00450-016-0310-y

Published: 01 March 2017 Publication History

Abstract

The collection of detailed consumption data through smart metering has led to privacy concerns. Aggregating the consumption data over a number of smart meters can be used to strike a balance between functional and privacy requirements. A number of contributions have proposed the use of differential privacy in smart metering to perturb aggregates in order to provide a proven privacy property for end consumers. However, as differential privacy has originally been proposed for very large datasets, the applicability in real-world smart metering is not guaranteed. In this paper, the effect of differential privacy on real smart metering data is studied, especially with respect to balancing utility and privacy requirements. The main finding is that even after some improvements of the basic method the aggregation group size must be of the order of thousands of smart meters in order to have reasonable utility.

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Cited By

Ghosh SAlam MDey SMukhopadhyay D(2024)“Hello? Is There Anybody in There?” Leakage Assessment of Differential Privacy Mechanisms in Smart Metering InfrastructureApplied Cryptography and Network Security10.1007/978-3-031-54776-8_7(163-189)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-54776-8_7
Hassan MRehmani MDu JChen J(2023)Differentially Private Demand Side Management for Incentivized Dynamic Pricing in Smart Grid¹1.
A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.
IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315747235:6(5724-5737)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3157472
Brunn Cvon Voigt STschorsch F(2023)Analyzing Continuous K-Anonymization for Smart Meter DataComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54204-6_16(272-282)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-54204-6_16
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cover image Computer Science - Research and Development

Computer Science - Research and Development Volume 32, Issue 1-2

March 2017

243 pages

ISSN:1865-2034

Issue’s Table of Contents

Copyright © Copyright © 2017 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2017

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Cited By

Ghosh SAlam MDey SMukhopadhyay D(2024)“Hello? Is There Anybody in There?” Leakage Assessment of Differential Privacy Mechanisms in Smart Metering InfrastructureApplied Cryptography and Network Security10.1007/978-3-031-54776-8_7(163-189)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-54776-8_7
Hassan MRehmani MDu JChen J(2023)Differentially Private Demand Side Management for Incentivized Dynamic Pricing in Smart Grid¹1.
A preliminary version has been published by 2020 IEEE International Conference on Communications (ICC 2020), June, 2020, Dublin, Ireland entitled Differentially Private Dynamic Pricing for Efficient Demand Response in Smart Grid.
IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315747235:6(5724-5737)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3157472
Brunn Cvon Voigt STschorsch F(2023)Analyzing Continuous K-Anonymization for Smart Meter DataComputer Security. ESORICS 2023 International Workshops10.1007/978-3-031-54204-6_16(272-282)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-54204-6_16
Zeng ZWang XLiu YChang L(2022)MSDA: multi-subset data aggregation scheme without trusted third partyFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-021-0316-x16:1Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11704-021-0316-x
Leukam Lako FLajoie-Mazenc PLaurent M(2021)Privacy-Preserving Publication of Time-Series Data in Smart GridSecurity and Communication Networks10.1155/2021/66435662021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6643566
Marks JMontano BChong JRaavi MIslam RCerny TShin DHung CHong JBechini ASong E(2021)Differential privacy applied to smart metersProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442360(761-770)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442360
Shahani SVenkateswaran RAbraham J(2021)Cost-based recommendation of parameters for local differentially private data aggregationComputers and Security10.1016/j.cose.2020.102144102:COnline publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1016/j.cose.2020.102144
Hassan MRehmani MChen J(2020)Differential Privacy Techniques for Cyber Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2019.294474822:1(746-789)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.1109/COMST.2019.2944748
Hale MBarooah PParker KYazdani K(2019)Differentially Private Smart MeteringProceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization10.1145/3363459.3363530(33-42)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3363459.3363530

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