Membership Inference Attacks on Aggregated Time Series with Linear Programming

Antonin Voyez, Antonin Voyez, Tristan Allard, Gildas Avoine, Gildas Avoine, Pierre Cauchois, Elisa Fromont, Elisa Fromont, Matthieu Simonin

2022

Abstract

Aggregating data is a widely used technique to protect privacy. Membership inference attacks on aggregated data aim to infer whether a specific target belongs to a given aggregate. We propose to study how aggregated time series data can be susceptible to simple membership inference privacy attacks in the presence of adversarial background knowledge. We design a linear programming attack that strongly benefits from the number of data points published in the series and show on multiple public datasets how vulnerable the published data can be if the size of the aggregated data is not carefully balanced with the published time series length. We perform an extensive experimental evaluation of the attack on multiple publicly available datasets. We show the vulnerability of aggregates made of thousands of time series when the aggregate length is not carefully balanced with the published length of the time series.

Paper Citation

in Harvard Style

Voyez A., Allard T., Avoine G., Cauchois P., Fromont E. and Simonin M. (2022). Membership Inference Attacks on Aggregated Time Series with Linear Programming. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 193-204. DOI: 10.5220/0011276100003283

in Bibtex Style

@conference{secrypt22,
author={Antonin Voyez and Tristan Allard and Gildas Avoine and Pierre Cauchois and Elisa Fromont and Matthieu Simonin},
title={Membership Inference Attacks on Aggregated Time Series with Linear Programming},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={193-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011276100003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Membership Inference Attacks on Aggregated Time Series with Linear Programming
SN - 978-989-758-590-6
AU - Voyez A.
AU - Allard T.
AU - Avoine G.
AU - Cauchois P.
AU - Fromont E.
AU - Simonin M.
PY - 2022
SP - 193
EP - 204
DO - 10.5220/0011276100003283