22 citations · 65 across the 7 of their papers we have counts for
10 papers
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Xinyu Tang, Saeed Mahloujifar, Liwei Song +4
Membership inference attacks are a key measure to evaluate privacy leakage in machine learning (ML) models. These attacks aim to distinguish training members from non-members by ex…
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
Alireza Bahramali, Milad Nasr, Amir Houmansadr +2
Deep Neural Networks (DNNs) have become prevalent in wireless communication systems due to their promising performance. However, similar to other DNN-based applications, they are v…
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr, Shuang Song, Abhradeep Thakurta +2
Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, wh…
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising
Milad Nasr, Reza Shokri, Amir houmansadr
Deep learning models leak significant amounts of information about their training datasets. Previous work has investigated training models with differential privacy (DP) guarantees…
Blind Adversarial Network Perturbations
Milad Nasr, Alireza Bahramali, Amir Houmansadr
Deep Neural Networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e.g., stat…
Bidding Strategies with Gender Nondiscrimination: Constraints for Online Ad Auctions
Milad Nasr, Michael Tschantz
Interactions between bids to show ads online can lead to an advertiser's ad being shown to more men than women even when the advertiser does not target towards men. We design biddi…