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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…
Membership Privacy for Machine Learning Models Through Knowledge Transfer
Virat Shejwalkar, Amir Houmansadr
Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's trainin…
Fundamental Limits of Covert Packet Insertion
Ramin Soltani, Dennis Goeckel, Don Towsley +1
Covert communication conceals the existence of the transmission from a watchful adversary. We consider the fundamental limits for covert communications via packet insertion over pa…
Asymptotic Limits of Privacy in Bayesian Time Series Matching
Nazanin Takbiri, Dennis L. Goeckel, Amir Houmansadr +1
Various modern and highly popular applications make use of user data traces in order to offer specific services, often for the purpose of improving the user's experience while usin…