Differentially Private Outlier Detection in Multivariate Gaussian Signals
Kwassi Holali Degue, Karthik Gopalakrishnan, Max Z. Li, Hamsa Balakrishnan, Jérôme Le Ny · 2021
The detection of outliers in data, while preserving the privacy of individual agents who contributed to the data set, is an increasingly important task when monitoring and controlling large-scale systems. In this paper, we use an algorithm based on the sparse vector technique to perform differentially private outlier detection in multivariate Gaussian signals. Specifically, we derive analytical expressions to quantify the trade-off between detection accuracy and privacy. We validate our analytical results through numerical simulations.