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 differential...

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Bibliographic Details
Main Authors: Degue, Kwassi H., Gopalakrishnan, Karthik, Li, Max Z., Balakrishnan, Hamsa, Ny, Jerome Le
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Closed-form solutions
Control systems
Data privacy
Data storage systems
Differential privacy
Large-scale systems
Numerical simulation
Outlier detection
Pattern recognition and classification
Privacy
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Summary: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.
ISSN:2378-5861
DOI:10.23919/ACC50511.2021.9483171