Deter

The threat and impact of cybersecurity breaches are felt throughout society with massive financial losses to businesses and breach of national secrets. Human behavior is increasing seen as a fundamental security vulnerability that is at the center of many security breaches. Several approaches have been used for improving user security behavior, including enacting information security policies, providing security awareness training, and introducing penalties for security violations; these approaches have not been very effective.

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.

Our world has become increasingly reliant on integrated circuits (ICs). Mobile phones are deeply enmeshed in our everyday lives, we drive cars equipped with hundreds of ICs, and have come to depend on the power grid and other cyber physical systems that are controlled by ICs. Not surprisingly, the issue of securing hardware has become increasingly vital. A reverse engineering adversary may, for example, be motivated by extracting intellectual property from a circuit, cloning a design for product piracy, or creating a targeted backdoor for stealing cryptographic keys.

Emerging intelligent automobiles will be able to harness advance on-car sensors to support new applications such as pollution detection, road condition monitoring, and traffic control. All these applications require the ability to verify both the location and the time of a reading. This project involves the design of verification methods that make use of environment factors, such as the presence of light and shadows and the measured wireless signal strength, instead of conventional public key infrastructure-based methods, in order to verify when and where data was collected.

In a crowdsensing system, energy efficient data collection is a primary concern for mobile sensing service providers (i.e., mobile users offering sensing as a service via built-in sensors on their mobile devices) in order to maximize battery life whereas trustworthiness is a primary concern for the end users. The proposed research will simultaneously address energy-efficient data collection and context-aware incentives to both minimize power consumption and maximize data trustworthiness.