CAREER: A Unified Analytics Stack for Secure Computation

This project introduces a unified software stack for secure computation that integrates cryptographic and hardware-based techniques, each with its own unique strengths, challenges, and performance characteristics. The project’s novelties include (i) software abstractions and intermediate representations that allow reusing functionality across technologies and workloads, (ii) a distributed and fault-tolerant system runtime for secure data analysis pipelines, and (iii) a versatile performance modeling and optimization framework that integrates diverse cost metrics to efficiently deploy secure data pipelines in heterogeneous environments. The project’s broader significance is the potential to enable secure analytics in a scalable fashion; an ability that will have implications on how modern society protects privacy and intellectual property while extracting value from data. The project includes three complementary thrusts that focus on software abstractions, scalable workload distribution, and cost-based optimization. The project designs a unified software architecture for secure analytics that supports diverse technologies (fully homomorphic encryption, secure multiparty computation, trusted execution environments) and workloads (machine learning, relational analytics, time series computations) on top of the same oblivious execution engine. Second, the project develops a novel parallel and distributed system runtime that scales secure computation within and across machines, leveraging heterogeneous resources and ensuring transparent fault tolerance. Finally, the project introduces original cost-based optimization techniques that incorporate performance objectives, threat models, and monetary budgets to enable automated planning of secure data pipelines. The project aims to make privacy-enhancing technologies a core component of the computer science education and to lay the foundation for a new generation of secure computing systems by rethinking the entire analytics stack: from the programming abstractions all the way down to the hardware. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2541869 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ioannis Liagouris | Institution: Trustees of Boston University, BOSTON, MA | Award Amount: $422,683 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541869 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541869.html