1.6k citations · 2.1k across the 35 of their papers we have counts for
5 papers · 1 filter
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…
FRL: Federated Rank Learning
Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr
Federated learning (FL) allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data among eac…
Superstring-Based Sequence Obfuscation to Thwart Pattern Matching Attacks
Bo Guan, Nazanin Takbiri, Dennis Goeckel +2
User privacy can be compromised by matching user data traces to records of their previous behavior. The matching of the statistical characteristics of traces to prior user behavior…
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz +1
While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not full…
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…