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

TL;DR

This paper demonstrates that fine-grained electrical consumption data from smart meters is highly unique, posing significant privacy risks, as individuals can often be re-identified even with degraded data.

Contribution

It provides the first large-scale analysis of the uniqueness of real-life smart meter data and its implications for privacy-preserving data sharing.

Findings

Over 90% re-identification with 5 consecutive measures

High uniqueness persists even with data rounding to 100 watts

Strong correlation between uniqueness, entropy, and temperature

Abstract

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.