Transition to Practice

Many challenging real world problems, e.g., voting and blind auction, require computation over sensitive data supplied by multiple mutually-distrustful entities. Elegant cryptographic theories have been developed to solve these problems without relying on a mutually-trusted third party. Practitioners also built prototypes capable of securely computing set intersection, AES encryption, Hamming distance, etc. However, many other applications, such as data mining and running universal machines, are far more complex than what can be supported by the state-of-the-art techniques.

Increasingly medical devices are dependent on software and the wireless channel for their operations, which also pose new vulnerabilities to their safe, dependable, and trustworthy operations. Medical devices such as implantable insulin pumps, which are in wide use today, continuously monitor and manage a patient's diabetes without the need for frequent daily patient interventions. These devices, not originally designed against cyber security threats, must now mitigate these threats.

The project aims at quantifying a general network's inner potential for supporting various forms of security by achieving secret common randomness between pairs or groups of its nodes. Statistical and computational secrecy measures are being considered against a general passive adversary. Common-randomness-achieving protocols are classified into two groups: culture-building and crowd-shielding. The former achieves common randomness between nodes situated in close proximity of each other, from correlated observations of specific (natural or induced) network phenomena.

Interoperable, reconfigurable systems of medical devices are the future of medical technology. They will improve care outcomes by catching common mistakes, reduce clinician cognitive workload by suppressing false alarms, and streamline and simplify continued care, especially when patients move between different medical facilities. As the penetration of "smart" networked medical technology increases, we will see increased problems with cybersecurity of such systems.

Information technology, advances in statistical computing, and the deluge of data available through the Internet are transforming computational social science. However, a major challenge is maintaining the privacy of human subjects. This project is a broad, multidisciplinary effort to help enable the collection, analysis, and sharing of sensitive data while providing privacy for individual subjects.