The Bounded Gaussian Mechanism for Differential Privacy

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Bo Chen
Matthew Hale


The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.

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How to Cite
Chen, Bo, and Matthew Hale. 2024. “The Bounded Gaussian Mechanism for Differential Privacy”. Journal of Privacy and Confidentiality 14 (1).
Author Biography

Matthew Hale, University of Florida

Matthew Hale is an Assistant Professor of Mechanical and Aerospace Engineering at the University of Florida. He received his BSE in Electrical Engineering from the University of Pennsylvania in 2012, and his MS and PhD in Electrical and Computer Engineering from the Georgia Institute of Technology in 2015 and 2017, respectively. His research interests include multi-agent systems, privacy in control, mobile robotics, and distributed optimization. He received an NSF CAREER Award in 2020, an ONR YIP in 2022, and a 2022 Excellence Award for Assistant Professors at the University of Florida (for being one of the 10 most outstanding assistant professors across all disciplines at the university).

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