Information virality is an increasingly important topic in modern media environments, but it often remains overlooked in discussions about information security. This presentation will explain why ...
Dr. Jason Reinhardt has been doing systematic strategy analysis and development for 18 years, both within and outside government. His work has focused on the application of quantitative risk ...
Advances in AI and big data analytics rely on data sharing, which can be impeded by privacy concerns. Most challenging in privacy protection is protection of data-in-use, since even encrypted data ...
Differential privacy (DP) is the state-of-the-art framework for formal privacy protection, but many available DP methods are designed primarily for estimation. On the other hand, in many scientific ...
Data privacy on edge devices has become a significant concern, given the widespread use of mobile phones. There is a potential risk of sensitive personal data being leaked through an insecure network ...
Over 33% of vehicles sold in 2021 had integrated autonomous driving (AD) systems. While many adversarial machine learning attacks have been studied against these systems, they all require an adversary ...
Differential privacy (DP) has arisen as the state-of-the-art framework for formal privacy protection when analyzing sensitive data. However, the fundamentals of DP are still not well understood. There ...
As more personal data is collected and analyzed, there is a growing need for formal privacy protection. Differential privacy (DP) has arisen as the state-of-the-art method in privacy protection, but ...
The Center for Education and Research in Information Assurance and Security (CERIAS) is currently viewed as one of the ...
Vehicular ad hoc networks (VANETs) are gaining more and more interest in intelligence transportation system research fields. They allow optimized traffic management due to improved vehicle resource ...
We are working on various (static + dynamic) techniques to secure bootloaders, especially UEFI/EDK-2.