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A study on data de-pseudonymization in the smart grid

Published: 21 April 2015 Publication History

Abstract

In the transition to the smart grid, the electricity networks are becoming more data intensive with more data producing devices deployed, increasing both the opportunities and challenges in how the collected data are used. For example, in the Advanced Metering Infrastructure (AMI) the devices and their corresponding data give more information about the operational parameters of the environment but also details about the habits of the people living in the houses monitored by smart meters. Different anonymization techniques have been proposed to minimize privacy concerns, among them the use of pseudonyms. In this work we return to the question of the effectiveness of pseudonyms, by investigating how a previously reported methodology for de-pseudonymization performs given a more realistic and larger dataset than was previously used. We also propose and compare the results with our own simpler de-pseudonymization methodology.
Our results indicate, not surprisingly, that large realistic datasets are very important to properly understand how an experimental method performs. Results based on small datasets run the risk of not being generalizable. In particular, we show that the number of re-identified households by breaking pseudonyms is dependent on the size of the dataset and the period where the pseudonyms are constant and not changed. In the setting of the smart grid, results will even vary based on the season when the dataset was captured. Knowing that relative simple changes in the data collection procedure may significantly increase the resistance to de-anonymization attacks will help future AMI deployments.

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Cited By

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  • (2024)Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling AttacksProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653272(295-306)Online publication date: 19-Jun-2024
  • (2024)Energy Disaggregation Risk Resilience through Microaggregation and Discrete Fourier TransformInformation Sciences10.1016/j.ins.2024.120211(120211)Online publication date: Jan-2024
  • (2022)DFTMicroagg: a dual-level anonymization algorithm for smart grid dataInternational Journal of Information Security10.1007/s10207-022-00612-821:6(1299-1321)Online publication date: 7-Sep-2022
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cover image ACM Conferences
EuroSec '15: Proceedings of the Eighth European Workshop on System Security
April 2015
51 pages
ISBN:9781450334792
DOI:10.1145/2751323
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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Publication History

Published: 21 April 2015

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Author Tags

  1. AMI data de-pseudonymization
  2. AMI privacy
  3. smart grid data

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EuroSys '15
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EuroSys '15: Tenth EuroSys Conference 2015
April 21, 2015
Bordeaux, France

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Cited By

View all
  • (2024)Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling AttacksProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653272(295-306)Online publication date: 19-Jun-2024
  • (2024)Energy Disaggregation Risk Resilience through Microaggregation and Discrete Fourier TransformInformation Sciences10.1016/j.ins.2024.120211(120211)Online publication date: Jan-2024
  • (2022)DFTMicroagg: a dual-level anonymization algorithm for smart grid dataInternational Journal of Information Security10.1007/s10207-022-00612-821:6(1299-1321)Online publication date: 7-Sep-2022
  • (2022)Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure RiskDatabase and Expert Systems Applications10.1007/978-3-031-12423-5_6(71-84)Online publication date: 29-Jul-2022
  • (2020)The Challenges of Privacy and Access Control as Key Perspectives for the Future Electric Smart GridIEEE Open Journal of the Communications Society10.1109/OJCOMS.2020.30375171(1934-1960)Online publication date: 2020
  • (2020)Smart Meter Data Obfuscation Using Correlated NoiseIEEE Internet of Things Journal10.1109/JIOT.2020.29832137:8(7250-7264)Online publication date: Aug-2020
  • (2020)Reconciling Privacy and Utility for Energy Services – an Application to Demand Response Protocols2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW51379.2020.00054(348-355)Online publication date: Sep-2020
  • (2018)Privacy Risks in Resource Constrained Smart Micro-Grids2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2018.00139(527-532)Online publication date: May-2018
  • (2018)Inferring Private User Behaviour Based on Information LeakageSmart Micro-Grid Systems Security and Privacy10.1007/978-3-319-91427-5_7(145-159)Online publication date: 28-Aug-2018
  • (2017)An MPC-based protocol for secure and privacy-preserving smart metering2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)10.1109/ISGTEurope.2017.8260202(1-6)Online publication date: Sep-2017
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