Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks-Reference-Cited by-同舟云学术

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Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Published:2024-06-19 Issue: Volume:28 Page:295-306
ISSN:
Container-title:Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy
language:
Short-container-title:

Author:

Cretu Ana-Maria¹^ORCID,Rusu Miruna²^ORCID,de Montjoye Yves-Alexandre²^ORCID

Affiliation:

1. EPFL, Lausanne, Switzerland

2. Imperial College London, London, United Kingdom

Funder

PETRAS National Centre of Excellence for IoT Systems Cybersecurity

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626232.3653272

Reference60 articles.

1. Citizens Advice. [n. d.]. Save money on your gas and electricity. [Online]. Available: https://www.citizensadvice.org.uk/consumer/energy/energy-supply/get-a-better-energy-deal/save-money-on-your-gas-and-electricity/. Accessed: 14th December 2022.

2. Clément Alaton and Frédéric Tounquet. 2020. Benchmarking smart metering deployment in the EU-28. Final Report (2020).

3. Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

4. IOT Analytics. [n.,d.]. Smart Meter Market 2019: Global penetration reached 14% -- North America, Europe ahead. [Online]. Available: https://iot-analytics.com/smart-meter-market-2019-global-penetration-reached-14-percent/. Accessed: 10th January 2021.

5. Article 29 Data Protection Working Party. 2011. Opinion 12/2011 on smart metering. https://ec.europa.eu/justice/article-29/documentation/opinion-recommendation/files/2011/wp183_en.pdf.

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