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Differentially Private Reward Functions for Markov Decision Processes

https://doi.org/10.1109/CCTA60707.2024.10666610

Benvenuti, Alexander; Hawkins, Calvin; Fallin, Brandon; Chen, Bo; Bialy, Brendan; Dennis, Miriam; Hale, Matthew (August 2024, IEEE)

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The Bounded Gaussian Mechanism for Differential Privacy

https://doi.org/10.29012/jpc.850

Chen, Bo; Hale, Matthew (February 2024, Journal of Privacy and Confidentiality)

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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Guaranteed Feasibility in Differentially Private Linearly Constrained Convex Optimization

https://doi.org/10.1109/LCSYS.2024.3513232

Benvenuti, Alexander; Bialy, Brendan; Dennis, Miriam; Hale, Matthew (January 2024, IEEE Control Systems Letters)

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