4 papers · 1 filter
Privacy Mechanism Design based on Empirical Distributions
Leonhard Grosse, Sara Saeidian, Mikael Skoglund +1
Pointwise maximal leakage (PML) is a per-outcome privacy measure based on threat models from quantitative information flow. Privacy guarantees with PML rely on knowledge about the…
A Tight Context-aware Privacy Bound for Histogram Publication
Sara Saeidian, Ata Yavuzyılmaz, Leonhard Grosse +2
We analyze the privacy guarantees of the Laplace mechanism releasing the histogram of a dataset through the lens of pointwise maximal leakage (PML). While differential privacy is c…
Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage
Sara Saeidian, Tobias J. Oechtering, Mikael Skoglund
Data-driven advancements significantly contribute to societal progress, yet they also pose substantial risks to privacy. In this landscape, differential privacy (DP) has become a c…
Rethinking Disclosure Prevention with Pointwise Maximal Leakage
Sara Saeidian, Giulia Cervia, Tobias J. Oechtering +1
This paper introduces a paradigm shift in the way privacy is defined, driven by a novel interpretation of the fundamental result of Dwork and Naor about the impossibility of absolu…