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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Milad Nasr, Reza Shokri, Amir Houmansadr
Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a compreh…
Fundamental Limits of Covert Bit Insertion in Packets
Ramin Soltani, Dennis Goeckel, Don Towsley +1
Covert communication is necessary when revealing the mere existence of a message leaks sensitive information to an attacker. Consider a network link where an authorized transmitter…
Asymptotic Loss in Privacy due to Dependency in Gaussian Traces
Nazanin Takbiri, Ramin Soltani, Dennis L. Goeckel +2
The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized,…
Fundamental Limits of Invisible Flow Fingerprinting
Ramin Soltani, Dennis Goeckel, Don Towsley +1
Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume…
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning
Milad Nasr, Alireza Bahramali, Amir Houmansadr
Flow correlation is the core technique used in a multitude of deanonymization attacks on Tor. Despite the importance of flow correlation attacks on Tor, existing flow correlation t…
Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, Amir Houmansadr
Machine learning models leak information about the datasets on which they are trained. An adversary can build an algorithm to trace the individual members of a model's training dat…