20 citations · 30 across the 6 of their papers we have counts for
7 papers
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Ce Feng, Nuo Xu, Wujie Wen +2
Differential privacy is a widely accepted measure of privacy in the context of deep learning algorithms, and achieving it relies on a noisy training approach known as differentiall…
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Nuo Xu, Binghui Wang, Ran Ran +2
Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training. In this paper, we propose…
Data-Driven Contract Design for Multi-Agent Systems with Collusion Detection
Nayara Aguiar, Parv Venkitasubramaniam, Vijay Gupta
In applications such as participatory sensing and crowd sensing, self-interested agents exert costly effort towards achieving an objective for the system operator. We study such a…
Modeling and Detection of Future Cyber-Enabled DSM Data Attacks using Supervised Learning
Kostas Hatalis, Parv Venkitasubramaniam, Shalinee Kishore
Demand-Side Management (DSM) is a vital tool that can be used to ensure power system reliability and stability. In future smart grids, certain portions of a customers load usage co…
An Information Theoretic Study of Timing Side Channels in Two-user Schedulers
Xun Gong, Negar Kiyavash, Parv Venkitasubramaniam
Timing side channels in two-user schedulers are studied. When two users share a scheduler, one user may learn the other user's behavior from patterns of service timings. We measure…
Mitigating Timing Side Channel in Shared Schedulers
Sachin Kadloor, Negar Kiyavash, Parv Venkitasubramaniam
In this work, we study information leakage in timing side channels that arise in the context of shared event schedulers. Consider two processes, one of them an innocuous process (r…