research-article

Lightweight privacy for smart metering data by adding noise

Authors:

Sebastian ClaußAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 531 - 538

https://doi.org/10.1145/2554850.2554982

Published: 24 March 2014 Publication History

Abstract

With a Smart Metering infrastructure, there are many motivations for power providers to collect high-resolution data of energy usage from consumers. However, this collection implies very detailed information about the energy consumption of consumers being monitored. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers but making the provision of certain services still possible? Clearly, this is a tradeoff between privacy and utility. There are approaches for preserving privacy in various ways, but many of them affect the data usefulness or are computationally expensive. In this paper, we propose and evaluate a lightweight approach for privacy and utility based on the addition of noise. Furthermore, using real consumers' data, we discuss the influence of the technique in various Smart Grid scenarios. Finally, we also design and evaluate possible attacks to our solution.

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Cited By

Pokhrel SHossain MWalid A(2024)Modeling Practically Private Wireless Vehicle to Grid System With Federated Reinforcement LearningIEEE Transactions on Services Computing10.1109/TSC.2023.334446017:3(1044-1055)Online publication date: May-2024
https://doi.org/10.1109/TSC.2023.3344460
Sivadevuni SMaiya RKothari RKrishna A(2024)Comprehensive Survey of Integration of Smart Meters with Renewable Energy Sources2024 9th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES63552.2024.10859617(402-408)Online publication date: 16-Dec-2024
https://doi.org/10.1109/ICCES63552.2024.10859617
AbdelRaouf HLong MTran PBaral LKhan ZMahmoud MFouda MIbrahem M(2024)Privacy Preservation Techniques in Smart Grids: Balancing Security and Utility in IoT-Driven Environments2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)10.1109/AIBThings63359.2024.10863022(1-6)Online publication date: 7-Sep-2024
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Index Terms

Lightweight privacy for smart metering data by adding noise

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Published In

cover image ACM Conferences

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Citations

Cited By

Pokhrel SHossain MWalid A(2024)Modeling Practically Private Wireless Vehicle to Grid System With Federated Reinforcement LearningIEEE Transactions on Services Computing10.1109/TSC.2023.334446017:3(1044-1055)Online publication date: May-2024
https://doi.org/10.1109/TSC.2023.3344460
Sivadevuni SMaiya RKothari RKrishna A(2024)Comprehensive Survey of Integration of Smart Meters with Renewable Energy Sources2024 9th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES63552.2024.10859617(402-408)Online publication date: 16-Dec-2024
https://doi.org/10.1109/ICCES63552.2024.10859617
AbdelRaouf HLong MTran PBaral LKhan ZMahmoud MFouda MIbrahem M(2024)Privacy Preservation Techniques in Smart Grids: Balancing Security and Utility in IoT-Driven Environments2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)10.1109/AIBThings63359.2024.10863022(1-6)Online publication date: 7-Sep-2024
https://doi.org/10.1109/AIBThings63359.2024.10863022
Sheikh NAsghar HFarokhi FKaafar M(2022)Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model ParametersIEEE Transactions on Services Computing10.1109/TSC.2021.310049815:6(3198-3209)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TSC.2021.3100498
Hossain MNatgunanathan IXiang YZhang Y(2022)Cost-Friendly Differential Privacy of Smart Meters Using Energy Storage and Harvesting DevicesIEEE Transactions on Services Computing10.1109/TSC.2021.308117015:5(2648-2657)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3081170
Tran HHu JPota H(2022)Smart Meter Data Obfuscation With a Hybrid Privacy-Preserving Data Publishing Scheme Without a Trusted Third PartyIEEE Internet of Things Journal10.1109/JIOT.2022.31530439:17(16080-16095)Online publication date: 1-Sep-2022
https://doi.org/10.1109/JIOT.2022.3153043
Khwaja AErkucuk SAnpalagan AVenkatesh B(2022)Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data ObfuscationIEEE Access10.1109/ACCESS.2022.315739010(27717-27735)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3157390
Kwankajornkeat SAswakul C(2022)Differential Private Motion Sensor and Wasted Energy in Building Energy Management SystemIEEE Access10.1109/ACCESS.2021.313840110(486-501)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3138401
Abreu ZPereira L(2022)Privacy protection in smart meters using homomorphic encryption: An overviewWIREs Data Mining and Knowledge Discovery10.1002/widm.146912:4Online publication date: 23-Jun-2022
https://doi.org/10.1002/widm.1469
Husnoo MAnwar AChakrabortty RDoss RRyan M(2021)Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2021.31243099(153276-153304)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124309
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