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Electrical Engineering and Systems Science > Signal Processing
Title: Differentially Private Communication of Measurement Anomalies in the Smart Grid
(Submitted on 4 Mar 2024 (v1), last revised 22 Mar 2024 (this version, v2))
Abstract: In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data. Our DP approach conceals consumption and system matrix data, while simultaneously enabling an untrusted third party to test hypotheses of anomalies, such as the presence of bad data, by releasing a randomized sufficient statistic for hypothesis-testing. We consider a measurement model corrupted by Gaussian noise and a sparse noise vector representing the attack, and we observe that the optimal test statistic is a chi-square random variable. To detect possible attacks, we propose a novel DP chi-square noise mechanism that ensures the test does not reveal private information about power injections or the system matrix. The proposed framework provides a robust solution for detecting bad data while preserving the privacy of sensitive power system data.
Submission history
From: Nikhil Ravi [view email][v1] Mon, 4 Mar 2024 18:55:16 GMT (442kb,D)
[v2] Fri, 22 Mar 2024 21:04:25 GMT (456kb,D)
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