  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.
