Differentially Private Outlier Detection in Multivariate Gaussian Signals

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.

Automated summary

This paper explores the detection of outliers in data while preserving individual privacy. The algorithm utilizes sparse vector technique in multivariate Gaussian signals and quantifies the trade-off between accuracy and privacy. The analytical results are validated through numerical simulations.