382 citations · 2.5k across the 96 of their papers we have counts for
12 papers · 1 filter
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Ashwinee Panda, Saeed Mahloujifar, Arjun N. Bhagoji +2
Federated learning is inherently vulnerable to model poisoning attacks because its decentralized nature allows attackers to participate with compromised devices. In model poisoning…
Data-Plane Security Applications in Adversarial Settings
Liang Wang, Prateek Mittal, Jennifer Rexford
High-speed programmable switches have emerged as a promising building block for developing performant data-plane applications. In this paper, we argue that the resource constraints…
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…
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai, Saeed Mahloujifar, Prateek Mittal
Deep neural networks are known to be vulnerable to adversarially perturbed inputs. A commonly used defense is adversarial training, whose performance is influenced by model capacit…
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Chong Xiang, Saeed Mahloujifar, Prateek Mittal
The adversarial patch attack against image classification models aims to inject adversarially crafted pixels within a restricted image region (i.e., a patch) for inducing model mis…
F-PKI: Enabling Innovation and Trust Flexibility in the HTTPS Public-Key Infrastructure
Laurent Chuat, Cyrill Krähenbühl, Prateek Mittal +1
We present F-PKI, an enhancement to the HTTPS public-key infrastructure (or web PKI) that gives trust flexibility to both clients and domain owners, and enables certification autho…