Publications (7)
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
Ismat Jarin, Birhanu Eshete
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regula…
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
Mengwei Yang, Ismat Jarin, Baturalp Buyukates +2
Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. One key challenge in practical FL systems is data heterogeneity, particu…
VR ProfiLens: User Profiling Risks in Consumer Virtual Reality Apps
Ismat Jarin, Olivia Figueira, Yu Duan +2
Virtual reality (VR) platforms and apps collect user sensor data, including motion, facial, eye, and hand data, in abstracted form. These data may expose users to unique privacy ri…
BehaVR: User Identification Based on VR Sensor Data
Ismat Jarin, Yu Duan, Rahmadi Trimananda +3
Virtual reality (VR) platforms enable a wide range of applications, however, pose unique privacy risks. In particular, VR devices are equipped with a rich set of sensors that colle…
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members
Ismat Jarin, Birhanu Eshete
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses c…
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning
Ismat Jarin, Birhanu Eshete
Differential Privacy (DP) has emerged as a rigorous formalism to reason about quantifiable privacy leakage. In machine learning (ML), DP has been employed to limit inference/disclo…