2 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Sequential Auditing for f-Differential Privacy
Tim Kutta, Martin Dunsche, Yu Wei +1
We present new auditors to assess Differential Privacy (DP) of an algorithm based on output samples. Such empirical auditors are common to check for algorithmic correctness and imp…
General-Purpose -DP Estimation and Auditing in a Black-Box Setting
Önder Askin, Holger Dette, Martin Dunsche +4
In this paper we propose new methods to statistically assess -Differential Privacy (-DP), a recent refinement of differential privacy (DP) that remedies certain weaknesses of…
Concurrent Asynchronous Byzantine Agreement in Expected-Constant Rounds, Revisited
Ran Cohen, Pouyan Forghani, Juan Garay +2
It is well known that without randomization, Byzantine agreement (BA) requires a linear number of rounds in the synchronous setting, while it is flat out impossible in the asynchro…
The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
Yu Wei, Yun Lu, Malik Magdon-Ismail +1
We investigate the privacy of {\em any} algorithm whose outputs have Gaussian distribution. This work is motivated by the prevalence of such algorithms in several useful (ML) appli…
How Private Are Commonly-Used Voting Rules?
Ao Liu, Yun Lu, Lirong Xia +1
Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting sc…