12 citations · 12 across the 6 of their papers we have counts for
6 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…
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…
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…
Information Density Bounds for Privacy
Sara Saeidian, Leonhard Grosse, Parastoo Sadeghi +2
This paper explores the implications of guaranteeing privacy by imposing a lower bound on the information density between the private and the public data. We introduce a novel and…
Quantifying Privacy via Information Density
Leonhard Grosse, Sara Saeidian, Parastoo Sadeghi +2
We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we p…
Extremal Mechanisms for Pointwise Maximal Leakage
Leonhard Grosse, Sara Saeidian, Tobias Oechtering
Data publishing under privacy constraints can be achieved with mechanisms that add randomness to data points when released to an untrusted party, thereby decreasing the data's util…