326 citations · 600 across the 23 of their papers we have counts for
6 papers · 1 filter
Cluster Paths: Navigating Interpretability in Neural Networks
Nicholas M. Kroeger, Vincent Bindschaedler
While modern deep neural networks achieve impressive performance in vision tasks, they remain opaque in their decision processes, risking unwarranted trust, undetected biases and u…
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…
Towards Privacy-preserving Photorealistic Self-avatars in Mixed Reality
Ethan Wilson, Vincent Bindschaedler, Sophie Jörg +3
Photorealistic 3D avatar generation has rapidly improved in recent years, and realistic avatars that match a user's true appearance are more feasible in Mixed Reality (MR) than eve…
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…