29 citations · 46 across the 12 of their papers we have counts for
5 papers · 1 filter
Contraction and Statistical Inference under Privacy for Uniformly Bounded Distributions
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We investigate -interior pointwise maximal leakage (PML) as a tool for contraction analyses and disclosure control. Based on the strong adversarial threat models from maximal le…
Information Leakage Envelopes
Sara Saeidian, Carlos Pinzón, Catuscia Palamidessi
We study privacy guarantees in the framework of pointwise maximal leakage (PML) that satisfy two requirements: they are robust under post-processing and upper bound the failure pro…
On the Information Leakage Envelope of the Gaussian Mechanism
Sara Saeidian
We study the pointwise maximal leakage (PML) envelope of the Gaussian mechanism, which characterizes the smallest information leakage bound that holds with high probability under a…
Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy
Leonhard Grosse, Sara Saeidian, Tobias J. Oechtering +1
We investigate Dobrushin coefficients of discrete Markov kernels that have bounded pointwise maximal leakage (PML) with respect to all distributions with a minimum probability mass…
Context-aware Privacy Bounds for Linear Queries
Heng Zhao, Sara Saeidian, Tobias J. Oechtering
Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy prote…