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The privacy cost of fine-grained electrical consumption data
Antonin Voyez1,2, Tristan Allard1, Gildas Avoine1,3
1University of Rennes, CNRS, IRISA, Rennes, France.
Scientific Reports
|May 19, 2025
Summary
Smart meter data reveals high household re-identification risk. Even degraded electrical consumption time series can expose over 40% of homes with just 7 data points, threatening privacy.
Area of Science:
- Computer Science
- Data Privacy
- Smart Grid Technology
Background:
- Smart meter deployment generates vast amounts of electrical consumption time-series data.
- This data is sensitive due to privacy concerns but valuable for open data initiatives.
- Balancing data utility with privacy protection is a key challenge in smart grid programs.
Purpose of the Study:
- To investigate the uniqueness of large-scale, real-life, fine-grained electrical consumption time-series.
- To assess the direct link between data uniqueness and potential privacy threats.
- To quantify the re-identification risk associated with electrical consumption data.
Main Methods:
- Analysis of a large dataset comprising 2.5 million half-hourly electrical time series.
- Evaluation of household re-identification rates based on consecutive electrical measures.
- Assessment of uniqueness under data degradation scenarios, including rounding to 100 watts.
Main Results:
- A worryingly high uniqueness rate was observed in the electrical consumption time-series dataset.
- Knowing just 5 consecutive measures re-identified over 90% of households on average.
- Even with data rounded to 100 watts, 7 consecutive measures re-identified over 40% of households.
Conclusions:
- Fine-grained electrical consumption time-series data exhibits significant uniqueness, posing substantial privacy risks.
- The re-identification risk persists even when data is severely degraded or rounded.
- Strong correlations exist between data uniqueness, entropy, electricity consumption, and temperature.
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