55 citations · 194 across the 35 of their papers we have counts for
12 papers · 1 filter
EIFFeL: Ensuring Integrity for Federated Learning
Amrita Roy Chowdhury, Chuan Guo, Somesh Jha +1
Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from th…
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Jiefeng Chen, Xi Wu, Yang Guo +2
There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021). Compare…
Lightweight, Multi-Stage, Compiler-Assisted Application Specialization
Mohannad Alhanahnah, Rithik Jain, Vaibhav Rastogi +2
Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. The…
NeuraCrypt is not private
Nicholas Carlini, Sanjam Garg, Somesh Jha +3
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the…
Fairness Properties of Face Recognition and Obfuscation Systems
Harrison Rosenberg, Brian Tang, Kassem Fawaz +1
The proliferation of automated face recognition in the commercial and government sectors has caused significant privacy concerns for individuals. One approach to address these priv…
Few-Shot Domain Adaptation For End-to-End Communication
Jayaram Raghuram, Yijing Zeng, Dolores García Martí +4
The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently be…