research-article

Defending against load monitoring in smart metering data through noise addition

Authors:

Hyggo AlmeidaAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 2218 - 2224

https://doi.org/10.1145/2695664.2695800

Published: 13 April 2015 Publication History

Abstract

Power providers have started replacing traditional electricity meters for Smart Meters, which can transmit current power consumption levels to the provider within short intervals. Based on this data, power providers can perform and improve many service activities such as differential tariffs and load predictions. However, this also threatens consumers' privacy, since specific activity or behavior patterns can be deduced from the finely granular meter readings. Consequently, a serious issue needs to be addressed: how to preserve the privacy of consumers while making the provision of certain services still possible? In this work, we evaluate a lightweight approach for privacy and utility based on noise addition. To validate the privacy using the approach, we perform and evaluate a Non-Intrusive Appliance Load Monitoring attack using real consumers' data. To validate the utility using the approach, we analyze and discuss the benefits that can be provided through the use of Smart Meters.

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Cited By

Hassan ADeka DDvorkin Y(2022)Privacy-Aware Load Ensemble Control: A Linearly-Solvable MDP ApproachIEEE Transactions on Smart Grid10.1109/TSG.2021.311437013:1(255-267)Online publication date: Jan-2022
https://doi.org/10.1109/TSG.2021.3114370
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
Khwaja AAnpalagan ANaeem MVenkatesh B(2020)Smart Meter Data Obfuscation Using Correlated NoiseIEEE Internet of Things Journal10.1109/JIOT.2020.29832137:8(7250-7264)Online publication date: Aug-2020
https://doi.org/10.1109/JIOT.2020.2983213
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Defending against load monitoring in smart metering data through noise addition

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Published In

cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 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 the author(s) 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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Published: 13 April 2015

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SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

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

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Cited By

Hassan ADeka DDvorkin Y(2022)Privacy-Aware Load Ensemble Control: A Linearly-Solvable MDP ApproachIEEE Transactions on Smart Grid10.1109/TSG.2021.311437013:1(255-267)Online publication date: Jan-2022
https://doi.org/10.1109/TSG.2021.3114370
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
Khwaja AAnpalagan ANaeem MVenkatesh B(2020)Smart Meter Data Obfuscation Using Correlated NoiseIEEE Internet of Things Journal10.1109/JIOT.2020.29832137:8(7250-7264)Online publication date: Aug-2020
https://doi.org/10.1109/JIOT.2020.2983213
Xie SZhang FLin HTian Y(2019)A New Secure and Anonymous Metering Scheme for Smart Grid CommunicationsEnergies10.3390/en1224475112:24(4751)Online publication date: 12-Dec-2019
https://doi.org/10.3390/en12244751
Xu RZeng QZhu LChi HDu XGuizani M(2019)Privacy Leakage in Smart Homes and Its Mitigation: IFTTT as a Case StudyIEEE Access10.1109/ACCESS.2019.29112027(63457-63471)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2911202
Chen YMartínez JCastillejo PLópez L(2018)A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart GridEnergies10.3390/en1111297211:11(2972)Online publication date: 1-Nov-2018
https://doi.org/10.3390/en11112972
Kement CGultekin HTavli BGirici TUludag S(2017)Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart GridIEEE Transactions on Industrial Informatics10.1109/TII.2017.271866613:6(3226-3235)Online publication date: Dec-2017
https://doi.org/10.1109/TII.2017.2718666
Abrishamchi MAbdullah ADavid Cheok ABielawski K(2017)Side channel attacks on smart home systems: A short overviewIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2017.8217429(8144-8149)Online publication date: Oct-2017
https://doi.org/10.1109/IECON.2017.8217429
Yang HLiang Cheng Mooi Choo Chuah (2016)Evaluation of utility-privacy trade-offs of data manipulation techniques for smart metering2016 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS.2016.7860526(396-400)Online publication date: Oct-2016
https://doi.org/10.1109/CNS.2016.7860526

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