Formal methods

As mobile and network technologies proliferate, so does society's awareness of the vulnerability of private data within cyberspace. Protecting private information becomes specially important, since researchers estimate that 87% of Americans can be identified by name and address, if their zip code, gender, and birthday are known to intruders. The goal of this proposal will be to develop a new set of verification tools, algorithms, and interfaces that enable secure, effective and unobtrusive management of users' private information.

Modern systems such as the electric smart grid consist of both cyber and physical components that must work together; these are called cyber-physical systems, or CPS. Securing such systems goes beyond just cyber security or physical security into cyber-physical security. While the threats multiply within a CPS, physical aspects also can reduce the threat space. Unlike purely cyber systems, such as the internet, CPS are grounded in physical reality.

This project is developing tools and techniques for cost-effective evaluation of the trustworthiness of mobile applications (apps). The work focuses on enterprise scenarios, in which personnel at a business or government agency use mission-related apps and access enterprise networks.

Attacks on software applications such as email readers and web browsers are common. These attacks can cause damages ranging from application malfunction, loss of private data, to a complete takeover of users' computers. One effective strategy for limiting the damage is to adopt the principle of least privilege in application design: the application is split into several protection domains and each domain is given only the necessary privileges to perform its task.

Information about individuals is collected by a variety of organizations including government agencies, banks, hospitals, research institutions, and private companies. In many cases, sharing this data among organizations can bring benefits in social, scientific, business, and security domains, as the collected information is of similar nature, of about similar populations. However, much of this collected data is sensitive as it contains personal information, or information that could damage an organization's reputation or competitiveness.