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arxiv: 1112.2680 · v1 · pith:C73SWVSFnew · submitted 2011-12-12 · 📊 stat.ME · cs.CR· cs.LG

Random Differential Privacy

classification 📊 stat.ME cs.CRcs.LG
keywords privacydifferentialdefinitionrandomaddinganalogdatabaseeffect
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We propose a relaxed privacy definition called {\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure. Random differential privacy requires that adding a {\em randomly drawn new observation} to a database will have small effect on the output. We show an analog of the composition property of differentially private procedures which applies to our new definition. We show how to release an RDP histogram and we show that RDP histograms are much more accurate than histograms obtained using ordinary differential privacy. We finally show an analog of the global sensitivity framework for the release of functions under our privacy definition.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Conformal-DP: A Density-Aware Mechanism for Differential Privacy over Riemannian Manifolds via Conformal Transformation

    cs.CR 2025-04 unverdicted novelty 7.0

    Conformal-DP applies conformal transformations to create a density-aware DP mechanism on Riemannian manifolds, proving ε-DP and deriving a closed-form geodesic error bound dependent only on density ratio and independe...