326 citations · 600 across the 23 of their papers we have counts for
4 papers · 1 filter
On the Choice of Databases in Differential Privacy Composition
Valentin Hartmann, Vincent Bindschaedler, Robert West
Differential privacy (DP) is a widely applied paradigm for releasing data while maintaining user privacy. Its success is to a large part due to its composition property that guaran…
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning
Wenxuan Bao, Luke A. Bauer, Vincent Bindschaedler
We study a pitfall in the typical workflow for differentially private machine learning. The use of differentially private learning algorithms in a "drop-in" fashion -- without acco…
Privacy accounting conomics: Improving differential privacy composition via a posteriori bounds
Valentin Hartmann, Vincent Bindschaedler, Alexander Bentkamp +1
Differential privacy (DP) is a widely used notion for reasoning about privacy when publishing aggregate data. In this paper, we observe that certain DP mechanisms are amenable to a…
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems
Hadi Abdullah, Aditya Karlekar, Saurabh Prasad +5
Audio CAPTCHAs are supposed to provide a strong defense for online resources; however, advances in speech-to-text mechanisms have rendered these defenses ineffective. Audio CAPTCHA…