In contrast to the traditional data communications models in which large blocks of data are compressed, the evolving information generation, access, and storage contexts require compressing relatively smaller blocks of data asynchronously and concurrently from a large number of sources, and often, with additional security and privacy constraints. This research addresses this growing need by developing a rigorous framework for universal lossy and lossless compression algorithms in the finite blocklength regime with strong theoretical guarantees. It also addresses the related need for privacy guarantees in low latency applications via new privacy metrics and mechanisms applicable to datasets of all sizes. Finally, the research will develop open-source computational tools for finite blocklength analysis, with applications to numerous communication and compression problems.
This research can be applied to the wide variety of systems that continuously collect, store, and process data, including online retailers, search engines, social network sites, and storage systems for electronic medical records. A key impact of the research will be much-needed compression techniques that are more suited to these low-latency applications than traditional methods. In addition, this research will develop privacy mechanisms for these applications, to provide solid privacy assurances in this modern environment in which users are increasingly concerned about their own sensitive data in various electronic forms. The research will also integrate undergraduate and graduate student involvement through curriculum development, theoretical research, and open-source software efforts.
