2 citations · 3 across the 5 of their papers we have counts for
5 papers
Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
Wenxuan Bao, Vincent Bindschaedler
There is a flurry of recent research papers proposing novel differentially private machine learning (DPML) techniques. These papers claim to achieve new state-of-the-art (SoTA) res…
Thwart Me If You Can: An Empirical Analysis of Android Platform Armoring Against Stalkerware
Malvika Jadhav, Wenxuan Bao, Vincent Bindschaedler
Stalkerware is a serious threat to individuals' privacy that is receiving increased attention from the security and privacy research communities. Existing works have largely focuse…
Inference Attacks for X-Vector Speaker Anonymization
Luke Bauer, Wenxuan Bao, Malvika Jadhav +1
We revisit the privacy-utility tradeoff of x-vector speaker anonymization. Existing approaches quantify privacy through training complex speaker verification or identification mode…
Provably Secure Covert Messaging Using Image-based Diffusion Processes
Luke A. Bauer, Wenxuan Bao, Vincent Bindschaedler
We consider the problem of securely and robustly embedding covert messages into an image-based diffusion model's output. The sender and receiver want to exchange the maximum amount…
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
Wenxuan Bao, Francesco Pittaluga, Vijay Kumar B G +1
Data augmentation techniques, such as simple image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when…