Gaussian differential privacy
PulseAugur coverage of Gaussian differential privacy — every cluster mentioning Gaussian differential privacy across labs, papers, and developer communities, ranked by signal.
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New Gaussian Approximation Enhances Differentially Private Algorithm Analysis
Researchers have developed a new method for analyzing the privacy guarantees of differentially private algorithms, specifically focusing on differentially private stochastic gradient descent (DP-SGD). The new approach u…
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New method enables one-shot differentially private confidence regions
Researchers have developed a novel framework for creating differentially private confidence regions with a single privacy cost. This method involves adding noise only to the final resampling quantile, rather than privat…
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Researchers propose $\mu \approx \varepsilon/5$ conversion for Gaussian differential privacy
Researchers have published a paper detailing methods for converting privacy parameters between pure differential privacy ($\varepsilon$) and Gaussian differential privacy (GDP, $\mu$). The study proposes principled mapp…
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New research advances differential privacy in ML for adaptation and testing
Researchers are developing new methods to ensure differential privacy in machine learning tasks, particularly for hypothesis testing and test-time adaptation. One paper introduces differentially private versions of popu…