  Recent advancements in research have shown the efficacy of employing sensor
measurements, such as voltage and power data, in identifying line outages
within distribution grids. However, these measurements inadvertently pose
privacy risks to electricity customers by potentially revealing their sensitive
information, such as household occupancy and economic status, to adversaries.
To safeguard raw data from direct exposure to third-party adversaries, this
paper proposes a novel decentralized data encryption scheme. The effectiveness
of this encryption strategy is validated via demonstration of its differential
privacy attributes by studying the Gaussian differential privacy. Recognizing
that the encryption of raw data could affect the efficacy of outage detection,
this paper analyzes the performance degradation by examining the
Kullback-Leibler divergence between data distributions before and after the
line outage. This analysis allows us to further alleviate the performance
degradation by designing an innovative detection statistic that accurately
approximates the optimal one. Manipulating the variance of this statistic, we
demonstrate its ability to approach the optimal detection performance. The
proposed privacy-aware detection procedure is evaluated using representative
distribution grids and real load profiles, covering 17 distinct outage
configurations. Our empirical results confirm the privacy-preserving nature of
our approach and show that it achieves comparable detection performance to the
optimal baseline.
