Date & Time:
Wednesday, January 4, 2012; 12:00 PM
In the field of Secure Multi-party Computation, the general objective is to design protocols that allow a group of parties to securely compute functions of their collective private data, while maintaining privacy (in that no parties reveal any more information about their personal data than necessary) and ensuring correctness (in that no parties can disrupt or influence the computation beyond the affect of changing their input data). Information theoretic approaches toward this broad problem, that provide provable (unconditional) security guarantees (even against adversaries that have unbounded computational power), have established that general computation is possible in a variety of scenarios. However, these general solutions are not always the most efficient or finely tuned to the requirements of specific problems and applications.
In this talk, we will overview our work toward the development of efficient information theoretic approaches for secure multi-party computation applications within the common theme of secure computation and inference over a distributed data network. These applications include:
1) private information retrieval, where the objective is to privately obtain data without revealing what was selected;
2) secure statistical analysis, the problem of extracting statistics without revealing anything else about the underlying distributed data;
3) secure sampling, which is the secure distributed generation of new data with a given joint distribution; and
4) secure authentication, where the identity of a party needs to authenticated via inference on his credentials and stored registration data.
Our contributions toward these applications include the following. We proposed a novel oblivious transfer protocol, applicable to private information retrieval, that trades off a small amount privacy for a drastic increase in efficiency. We leveraged a dimensionality reduction that exploits functional structure to simultaneously achieve arbitrarily high accuracy and efficiency in protocols that perform secure statistical analysis of distributed databases. Toward characterizing the region of distributions that can be securely sampled from scratch, we fully characterized the two-party scenario and provided inner and outer bounds on the multi-party scenario. Toward enabling secure distributed authentication, we proposed a two-factor secure biometric authentication system that is robust against the compromise of registered biometric data, allowing for revocability and providing resistance against cross-enrollment attacks.
Dr. Ye Wang
Ye Wang received the B.S. degree in electrical and computer engineering from Worcester Polytechnic Institute in 2005, and the M.S. and Ph.D. degrees in electrical and computer engineering from Boston University in 2009 and 2011, respectively. During his Ph.D. studies, he performed summer research internships at MIT Lincoln Laboratory in 2009, and in the Multimedia group at MERL in 2010. After completing his Ph.D. degree, he continued on as a postdoctoral researcher at the Information Systems and Sciences Laboratory at Boston University during the summer of 2011. He joined AgaMatrix, Inc. in September 2011, where he is currently a firmware engineer. His research interests are statistical security, information theory, statistical signal processing, and decision theory with applications to information security/assurance, and communication systems and networks.