Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be
robust to random perturbations of the input. However, these models are vulnerable to …

Building expressive encrypted databases that can scale to large volumes of data while enjoying
formal security guarantees has been one of the holy grails of security and cryptography …

Users struggle to adhere to expert-recommended security and privacy practices. While prior
work has studied initial adoption of such practices, little is known about the subsequent …

Motivated by the transformative impact of deep neural networks (DNNs) in various domains,
researchers and anti-virus vendors have proposed DNNs for malware detection from raw …

Side-channels are unanticipated information flows that present a significant threat to security
of systems. Quantitative analyses are required to measure the rate of information leakage …

Abstract Machine learning models often overfit to the training data and do not learn general
patterns like humans do. This allows an attacker to learn private membership or attributes …

The benefits of network coding in terms of throughput, security and robustness are well
understood for a large class of networks, such as wireless mesh networks and peer-to-peer …

We propose a probabilistic key distribution protocol for vehicular network that alleviates the
burden of traditional public-key infrastructures. Roadside units act as trusted nodes and are …

In this paper, we first present a theoretical framework aimed at generalizing the scaling laws
of delay, secrecy and throughput in mobile ad-hoc networks for various network models and …

Deep neural networks are susceptible to small-but-specific adversarial perturbations capable
of deceiving the network. This vulnerability can lead to potentially harmful consequences …