11 citations · 15 across the 6 of their papers we have counts for
5 papers · 1 filter
Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
Boyang Zhang, Istemi Ekin Akkus, Ruichuan Chen +4
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing and reasoning over diverse modalities, but their advanced abilities also raise sign…
Model Hijacking Attack in Federated Learning
Zheng Li, Siyuan Wu, Ruichuan Chen +6
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonom…
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX
Chengliang Zhang, Junzhe Xia, Baichen Yang +6
With the advancement of machine learning (ML) and its growing awareness, many organizations who own data but not ML expertise (data owner) would like to pool their data and collabo…
Cybercasing 2.0: You Get What You Pay For
Jaeyoung Choi, Istemi Ekin Akkus, Serge Egelman +4
Under U.S. law, marketing databases exist under almost no legal restrictions concerning accuracy, access, or confidentiality. We explore the possible (mis)use of these databases in…
The Case for a General and Interaction-based Third-party Cookie Policy
Istemi Ekin Akkus, Nicholas Weaver
The privacy implications of third-party tracking is a well-studied problem. Recent research has shown that besides data aggregators and behavioral advertisers, online social networ…