Broadly speaking, the intuition behind the “privacy amplification by subsampling” principle is that the privacy guarantees of a differentially private mechanism.
Jan 15, 2020 · Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms.
Jul 4, 2018 · In this paper we present a general method that recovers and improves prior analyses, yields lower bounds and derives new instances of privacy amplification by ...
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Our analysis relies on properties of divergences and privacy profiles, together with two additional ingredients. The first ingredient is a novel advanced joint ...
The privacy profiles machinery are applied to study the so-called ``privacy amplification by subsampling'' principle, which ensures that a differentially ...
In this paper we present a general method that recovers and improves prior analyses, yields lower bounds and derives new instances of privacy amplification by ...
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Broadly speaking, the intuition behind the “privacy amplification by subsampling” principle is that the privacy guarantees of a differentially private mechanism.
• Sublinear time algorithms in exploratory data analysis. • Sketching, mean ... Privacy amplification by subsampling: Tight analyses via couplings and.
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Nov 23, 2018 · Figure 1: Plots of privacy profiles. Results illustrate the notion of privacy profile and the different subsampling bounds derived in the paper.
Duration: 1:29:52
Posted: Oct 8, 2021
Posted: Oct 8, 2021
Missing: Profiles | Show results with:Profiles