26 citations · 27 across the 3 of their papers we have counts for
7 papers
MIMO-GAN: Generative MIMO Channel Modeling
Tribhuvanesh Orekondy, Arash Behboodi, Joseph B. Soriaga
We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distribution…
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries
Shadi Rahimian, Tribhuvanesh Orekondy, Mario Fritz
Machine learning models have been shown to leak information violating the privacy of their training set. We focus on membership inference attacks on machine learning models which a…
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., m…
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing…
Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
Machine Learning (ML) models are increasingly deployed in the wild to perform a wide range of tasks. In this work, we ask to what extent can an adversary steal functionality of suc…
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang +2
Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartp…