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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2022

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

Antonin Voyez
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Tristan Allard
Gildas Avoine
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Pierre Cauchois
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Elisa Fromont

Résumé

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.
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hal-03833605 , version 1 (09-11-2022)

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Antonin Voyez, Tristan Allard, Gildas Avoine, Pierre Cauchois, Elisa Fromont, et al.. Unique in the Smart Grid -The Privacy Cost of Fine-Grained Electrical Consumption Data. 2022. ⟨hal-03833605⟩
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