Enabling Data Privacy with GPU-Accelerated Encryption
ECE Professor David Kaeli, in collaboration with Ajay Joshi from Boston University, was awarded a $1.2M NSF grant for “Architecting GPUs for Practical Homomorphic Encryption-based Computing.”
Abstract Source: NSF
Cloud computing has become a popular path for efficiently sharing compute resources. However, the cloud can be an unsafe computing environment. To prevent data exposure, one can use Fully Homomorphic Encryption (FHE)-based computing in the cloud. FHE provides strong data privacy guarantees because it enables operations on encrypted data. Unfortunately, processing encrypted data using FHE takes multiple orders of magnitude longer than processing unencrypted data due to its prohibitively high compute and memory requirements. This project will explore the use of graphics processing units (GPUs) to accelerate FHE-based computing. We consider three different FHE schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski/Fan-Vercauteren (B/FV), and Cheon-Kim-Kim-Song (CKKS), thus supporting operations on both integers and floating-point numbers.
This project will advance the state-of-the-art in GPU compute and memory architectures to enable practical FHE-based computing in the cloud. We will also deliver new FHE benchmarks for GPUs and simulation tools. The outcomes of the proposed research will have a direct impact on the design of next-generation privacy-preserving computing systems. We will work with a network of companies to evaluate our work in a practical setting and disseminate it. We will also open-source software and tools resulting from our work to benefit the broader research community. We will actively participate in the Broadening Participation in Computing plans at both Northeastern University and Boston University, while developing a number of new programs to engage a diverse audience.