Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks

Kayode Adewole, Kayode Adewole, Kayode Adewole, Vicenç Torra

2023

Abstract

The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.

Paper Citation

in Harvard Style

Adewole K. and Torra V. (2023). Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks. In Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-666-8, SciTePress, pages 279-286. DOI: 10.5220/0011956800003555

in Bibtex Style

@conference{secrypt23,
author={Kayode Adewole and Vicenç Torra},
title={Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2023},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011956800003555},
isbn={978-989-758-666-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Privacy Protection of Synthetic Smart Grid Data Simulated via Generative Adversarial Networks
SN - 978-989-758-666-8
AU - Adewole K.
AU - Torra V.
PY - 2023
SP - 279
EP - 286
DO - 10.5220/0011956800003555
PB - SciTePress