Cryptography, applied

Secure computation allows users to collaboratively compute any program on their private data, while ensuring that they learn nothing beyond the output of the computation. Existing protocols for secure computation primarily rely on a boolean-circuit representation for the program being evaluated, which can be highly inefficient. This project focuses on developing secure-computation protocols in the RAM model of computation. Particularly challenging here is the need to ensure that memory accesses are oblivious, and do not leak information about private data.

Symmetric-key primitives are the lifeblood of practical cryptography, and are critical components of nearly any computer security system. The cryptographic community has developed a rich body of work on theoretically sound symmetric objects, but they are many orders of magnitude too slow for realistic usage. Thus, practitioners use fast primitives that have been designed to withstand known attacks, but which lack rigorous security guarantees based on natural mathematical problems.

A software implementation shows side-channel leakage when the physical effects of its implementation have a dependency to secret data such as cryptographic keys. Relevant physical effects include instruction execution time, memory access time, power consumption and electromagnetic radiation. Fifteen years after differential power analysis was first demonstrated, side-channel attacks are affecting software implementations in a broad variety of processors. Yet, without the support of automatic tools, programmers still have to resort to manual and error-prone insertion of countermeasures.

Operating systems (OS) form the core of the trusted computing base on most computer platforms. The security of a platform therefore crucially relies on the correct and secure operation of its OS. Unfortunately, malicious software such as rootkits infect the OS by compromising the integrity of its code and data, thereby jeopardizing the security of the entire platform.

Cybersecurity experts must possess several abilities: deep technical skills, the capability to recognize and respond to complex and emergent behavior, mastery of using abstractions and principles, the ability to assess risk and handle uncertainty, problem-solving and reasoning skills, and facility in adversarial thinking. Based on cognitive theory, this project will investigate the efficacy of model eliciting activities for developing students' ability to recognize and respond to complex and emergent behavior, and how to handle uncertainty and ambiguity.