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Membership Inference Attacks From First Principles
Nicholas Carlini, Steve Chien, Milad Nasr +3
A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dat…
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…