1.6k citations · 2.6k across the 33 of their papers we have counts for
6 papers · 1 filter
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri +1
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local model…
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning
Congzheng Song, Reza Shokri
Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning code…
On the Privacy Risks of Model Explanations
Reza Shokri, Martin Strobel, Yair Zick
Privacy and transparency are two key foundations of trustworthy machine learning. Model explanations offer insights into a model's decisions on input data, whereas privacy is prima…
Bypassing Backdoor Detection Algorithms in Deep Learning
Te Juin Lester Tan, Reza Shokri
Deep learning models are vulnerable to various adversarial manipulations of their training data, parameters, and input sample. In particular, an adversary can modify the training d…
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos
Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of t…
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Liwei Song, Reza Shokri, Prateek Mittal
The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, o…