91 citations · 113 across the 5 of their papers we have counts for
7 papers
Security Analysis of SplitFed Learning
Momin Ahmad Khan, Virat Shejwalkar, Amir Houmansadr +1
Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their…
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr
Semi-supervised machine learning (SSL) is gaining popularity as it reduces the cost of training ML models. It does so by using very small amounts of (expensive, well-inspected) lab…
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…
Quantifying Privacy Leakage in Graph Embedding
Vasisht Duddu, Antoine Boutet, Virat Shejwalkar
Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collect…
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri +1
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local model…
Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms
Nazanin Takbiri, Virat Shejwalker, Amir Houmansadr +2
The prevalence of mobile devices and Location-Based Services (LBS) necessitate the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBS…