Mingyue Ji and ECE Student Researchers

University of Utah Electrical and Computer Engineering professor Mingyue Ji, in collaboration with Daniela Tuninetti (University of Illinois Chicago) and Hua Sun (University of North Texas), has received a competitive Communication and Information Foundation NSF award to support his research: “Fundamental Limits of Cache-aided Multi-user Private Function Retrieval.” As the leading PI and institutional representative for this study, Ji leads a team of undergraduate and graduate ECE student researchers.

About the Research

For a computing system to be functional and effective, it must be able to handle three components: storage, computing, and communication. However, the slowest link of these three components sets the pace for the entire system. To minimize total computing time, therefore, joint occupation must be done to improve all three components.

Ji and his fellow researchers are working to design smart storage, computing, and communications to minimize the overall computation time while maintaining absolute privacy among system users. They will do so by working to develop a cache-aided multi-user private function retrieval.

Their research is motivated by the need to efficiently execute complex queries on massive databases in a way that minimizes the use of communication resources while preserving the privacy of the entity that initiated the query. Such complex queries are functions of the data points that are stored at remote servers (also known as edge servers or user devices).

Mingyue Ji

In addition to proposing new algorithms, Ji is working to categorize the fundamental limit of the absolute best that can be created using information theory.

Research Applications

Developing this high-speed and private computation system is crucial for systems where personal data is shared, with applications ranging from healthcare to education. For example, in artificial intelligence for healthcare, the sensitive patients’ data needs to be stored and shared in a private fashion for machine learning and data mining algorithm purposes. The proposed research of this project can achieve this goal with optimal efficiency. Ji says that the research will be incredibly valuable in “all applications that are privacy sensitive.”