The right to privacy has long been regarded as one of the basic universal human rights. The combination of ubiquitous sensors, wireless connectivity, and powerful recognition algorithms makes it easier than ever to monitor every aspect of our daily lives. From the use of sophisticated video surveillance systems to the theft of biometric signals, people are increasingly wary about the privacy of their multimedia data. To mitigate public concern over privacy violation, it is imperative to make privacy protection a priority in developing the next-generation multimedia processing algorithms. Due to the high dimensionality, high data-rates and stringent real-time requirements of multimedia systems, developing provably-secure privacy protection schemes for multimedia often leads to a blowup in complexity and remains impractical for most applications.
This research breaks this “efficiency barrier” of the classical cryptographical approach by investigating a new computational framework to combine distributed multimedia processing and homomorphic encryption. Building on recent results of our group, this work develops efficient encrypted-domain processing through optimal computational procedures, parallel computation in cipher-text, small-field manipulation and encrypted data compression. In addition, by exploiting the interplay between privacy and the perceptual nature of multimedia, this work develops a provably-secure tradeoff scheme between privacy and complexity to significantly reduce the complexity and bandwidth requirements of encrypted-domain processing. We demonstrate the usability of this new framework through novel applications in biometric matching, object detection, speech analysis and computational photography.
This work also incorporates outreach programs for high school students in rural areas, summer undergraduate research experiences, interdisciplinary postgraduate education and community outreach via television documentaries on research discovery.