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

arXiv: https://arxiv.org/abs/2211.07205 · HTML: https://mlsanity.com/paper/2211.07205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2022.

## Built on by (most cited first, 2 of 2)

- [Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks](https://mlsanity.com/paper/2404.03948/) (2024)
- [Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference](https://mlsanity.com/paper/2509.04169/) (2025)

## Abstract (arXiv)

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.