29 citations · 46 across the 12 of their papers we have counts for
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…
Bounds on the privacy amplification of arbitrary channels via the contraction of -divergence
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We examine the privacy amplification of channels that do not necessarily satisfy any LDP guarantee by analyzing their contraction behavior in terms of -divergence, an -dive…