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Feb 9, 2024 · In this work, we revisit the analysis of Report Noisy Max and Above Threshold with Gaussian noise and show that, under the additional assumption ...
Report Noisy Max and Above Threshold are two classical differentially private (DP) se- lection mechanisms. Their output is ob- tained by adding noise to a ...
Mar 21, 2024 · On the Privacy of Selection Mechanisms with Gaussian Noise. Jonathan Lebensold. Doina Precup. Borja Balle. McGill University, Mila. McGill ...
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The content discusses the analysis of privacy in selection mechanisms using Gaussian noise. It introduces Report Noisy Max and Above Threshold mechanisms, ...
Repository files navigation. README; MIT license. Repository for On the Privacy of Selection Mechanisms with Gaussian Noise. Jonathan Lebensold, Doina Precup ...
Abstract. A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset.
Feb 1, 2024 · In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP),
Feb 2, 2021 · If you use Gaussian noise on multiple statistics, each of sensitivity 1, and want to get the tightest possible privacy guarantees, then you ...
Mar 22, 2022 · In this paper, we propose a differential privacy strategy including encryption and decryption methods based on local features of non-Gaussian noise.
The Discrete Gaussian Expectation Maximization (Gradient) Algorithm ...
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Dec 30, 2021 · In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism ...