Awards

“Advanced data analytics,” says Brown CS faculty member Peihan Miao, “involves performing computation on data sets to produce useful insights. It’s found application across many fields and has enabled major breakthroughs, but its potential has been constrained by challenges in sharing sensitive, distributed data.” 

But secure multi-party computation (MPC), which allows multiple parties who have relevant datasets to perform computations on their combined data while preserving privacy, may offer a promising solution, and Peihan has just received a National Science Foundation (NSF) CAREER Award to help pursue it. CAREER Awards are given in support of outstanding junior faculty teacher-scholars who excel at research, education, and integration of the two within the context of an organizational mission.

“As a special case of MPC,” says Peihan, “private set intersection (PSI), which securely computes the data elements in common in the private sets, has shown early success in areas such as password breach detection and online advertising measurement. This project expands the initial success of PSI to a much broader range of applications of MPC.”

The project focuses on three main thrusts:

1. Bridging the gap between standard PSI and PSI with enriched functionalities by developing unified frameworks for private join and compute that computes arbitrary functions on the intersection and for fuzzy-matching PSI that identifies fuzzy or noisy matches

2. Studying large-scale PSI for big data, designing efficient protocols for PSI with unbalanced sets and resources and for streaming data, which are better suited for many real-world scenarios

3. Applying the new techniques to other important MPC problems, including privacy-preserving machine learning, genomic sequence matching, and private information retrieval

The project includes educational and outreach activities such as integrating research into curricula, organizing mentoring workshops, developing tutorial resources to guide researchers and developers in the field, and mentoring students at all levels, especially those from underrepresented groups in computing.

“The most novel aspects of the project,” Peihan says, “are identifying the fundamental challenges that currently limit the use cases of PSI and developing new tools that not only enhance PSI but also address common challenges in many other MPC problems. If successful, this could truly accelerate the industrial adoption of PSI and extend the frontiers of practical MPC, enabling large-scale, privacy-preserving data analytics on sensitive data.”

Peihan joins multiple previous Brown CS winners of the award, including (most recently) Vasileios Kemerlis, Srinath Sridhar, Malte Schwarzkopf, Daniel Ritchie, and George Konidaris.

For more information, click the link that follows to contact Brown CS Communications Manager Jesse C. Polhemus.