Chang Liu wins John Vlissides Award and a Best Paper Award

For his presentation entitled "Trace Oblivious Program Execution: A Programming Language Approach to Security," PhD candidate Chang Liu earned the John Vlissides Award at the ACM conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA) Doctoral Symposium held in Pittsburgh, Pennsylvania from October 23-30, 2015. OOPSLA is operated by SIGPLAN and was held as a part of SPLASH '15.  Liu’s advisors are Professor Michael Hicks and Associate Professor Elaine Shi (now at Cornell University).

The John Vlissides Award is given by a selection committee to a doctoral candidate who shows significant promise in applied software research.  Liu’s work involves developing ObliVM, a programming framework, which offers a domain-specific language for secure computation.  About the award, Liu said that he was surprised and pleased. “Actually it was a big surprise” he said of the award, “I had almost forgotten that there was an one [at the symposium].”

On November 15th, 2015, it was announced that the paper associated with Liu’s work was also awarded Best Paper for Applied Cyber Security Research at the Cyber Security Awareness Week conference at New York University.   

Liu earned his BS and MS from Shanghai Jiao Tong University, Shanghai, China before coming to the University of Maryland to earn his PhD in Computer Science. He says that is very interested in become a professor, and plans to search for a position this year.  Liu is currently at the University of California Berkeley, working with Professor Dawn Song.

A copy of Liu’s paper and power point slides from the conference may be found on his website.

An abstract of the paper is below:

We design and develop ObliVM, a programming framework for secure computation. ObliVM offers a domainspecific language designed for compilation of programs into efficient oblivious representations suitable for secure computation. ObliVM offers a powerful, expressive programming language and user-friendly oblivious programming abstractions. We develop  various showcase applications such as data mining, streaming algorithms, graph algorithms, genomic data analysis, and data structures, and demonstrate the scalability of ObliVM to bigger data sizes. We also show how ObliVM significantly reduces development effort while retaining competitive performance for a wide range of applications in comparison with hand-crafted solutions. We are in the process of open-sourcing ObliVM and our rich libraries to the community (www.oblivm.com), offering a reusable framework to implement and distribute new cryptographic algorithms.