Antonin Voyez

selected publications

1. arxiv

   Unique in the Smart Grid -The Privacy Cost of Fine-Grained Electrical Consumption Data

   Voyez, Antonin, Allard, Tristan,  Avoine, Gildas and 3 more authors

   Nov 2022

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   The collection of electrical consumption time series through smart meters grows with ambitious nationwide smart grid programs. This data is both highly sensitive and highly valuable: strong laws about personal data protect it while laws about open data aim at making it public after a privacy-preserving data publishing process. In this work, we study the uniqueness of large scale real-life fine-grained electrical consumption time-series and show its link to privacy threats. Our results show a worryingly high uniqueness rate in such datasets. In particular, we show that knowing 5 consecutive electric measures allows to re-identify on average more than 90% of households in our 2.5M half-hourly electric time series dataset. Moreover, uniqueness remains high even when data is severely degraded. For example, when data is rounded to the nearest 100 watts, knowing 7 consecutive electric measures allows to re-identify on average more than 40% of the households (same dataset). We also study the relationship between uniqueness and entropy, uniqueness and electric consumption, and electric consumption and temperatures, showing their strong correlation.

   @misc{voyez2022unique,
     year = {2022},
     month = nov,
     title = {Unique in the Smart Grid -The Privacy Cost of Fine-Grained Electrical Consumption Data},
     author = {Voyez, Antonin and Allard, Tristan and Avoine, Gildas and Cauchois, Pierre and Fromont, Elisa and Simonin, Matthieu},
     doi = {10.48550/ARXIV.2211.07205},
     url = {https://hal.archives-ouvertes.fr/hal-03833605v1},
     booktitle = {arXiv},
   }

2. SECRYPT

   Membership Inference Attacks on Aggregated Time Series with Linear Programming

   Voyez, Antonin, Allard, Tristan,  Avoine, Gildas and 3 more authors

   In 19th International Conference on Security and Cryptography Jul 2022

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   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.

   @inproceedings{voyez2022membership,
     year = {2022},
     month = jul,
     title = {Membership Inference Attacks on Aggregated Time Series with Linear Programming},
     author = {Voyez, Antonin and Allard, Tristan and Avoine, Gildas and Cauchois, Pierre and Fromont, Elisa and Simonin, Matthieu},
     booktitle = {19th International Conference on Security and Cryptography},
     organization = {SCITEPRESS-Science and Technology Publications},
     url = {https://hal.archives-ouvertes.fr/hal-03726234/},
   }