2 citations · 3 across the 8 of their papers we have counts for
10 papers
Let's Ask Gauss: Improved One-Run Privacy Auditing
Adya Agrawal, Yu Wei, Jaspal Singh +2
Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice. We stud…
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…
Pseudo-Equilibria, or: How to Stop Worrying About Crypto and Just Analyze the Game
Alexandros Psomas, Athina Terzoglou, Yu Wei +1
We consider the problem of a game theorist analyzing a game that uses cryptographic protocols. Ideally, a theorist abstracts protocols as ideal, implementation-independent primitiv…
Game of Trust: How Trustworthy Does Your Blockchain Think You Are?
Petros Drineas, Rohit Nema, Rafail Ostrovsky +1
We investigate how a blockchain can distill the collective belief of its nodes regarding the trustworthiness of a (sub)set of nodes into a {\em reputation system} that reflects the…
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…
Near-Optimal Communication Byzantine Reliable Broadcast under a Message Adversary
Timothé Albouy, Davide Frey, Ran Gelles +5
We address the problem of Reliable Broadcast in asynchronous message-passing systems with nodes, of which up to are malicious (faulty), in addition to a message adversary t…
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…
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
Maksim Tsikhanovich, Malik Magdon-Ismail, Muhammad Ishaq +1
Privacy is a major issue in learning from distributed data. Recently the cryptographic literature has provided several tools for this task. However, these tools either reduce the q…
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…