Data science

Computing on sensitive data is a standing challenge central to several modern-world applications. Secure Function Evaluation (SFE) allows mistrusting parties to jointly compute an arbitrary function on their private inputs without revealing anything but the result. The GC@Scale project focuses on novel scalable methods for addressing SFE, which directly translate to stronger cryptography and security for myriads of tasks with sensitive data.

The cybercriminal community is inarguably more organized, better resourced and more motivated than ever to perpetrate massive-scale computer infections across the Internet. The malware distribution systems that they control and operate are characterized by their use of highly specialized suppliers and commoditized malware services.

This research project studies security and privacy for wearable devices. Wearable computing is poised to become widely deployed throughout society. These devices offer many benefits to end users in terms of realtime access to information and the augmentation of human memory, but they are also likely to introduce new and complex privacy and security problems. People who use wearable devices need assurances that their privacy will be respected, and we also need ways to minimize the potential for wearable devices to intrude on the privacy of bystanders and others.

The domain name system (DNS) protocol plays a significant role in operation of the Internet by enabling the bi-directional association of domain names with IP addresses. It is also increasingly abused by malware, particularly botnets, by use of: (1) automated domain generation algorithms for rendezvous with a command-and-control (C&C) server, (2) DNS fast flux as a way to hide the location of malicious servers, and (3) DNS as a carrier channel for C&C communications.

One of the keys to scientific progress is the sharing of research data. When the data contain information about human subjects, the incentives not to share data are stronger. The biggest concern is privacy - specific information about individuals must be protected at all times. Recent advances in mathematical notions of privacy have raised the hope that the data can be properly sanitized and distributed to other research groups without revealing information about any individual. In order to make this effort worthwhile, the sanitized data must be useful for statistical analysis.