From Theory to Practice: Bridging the FHE Performance Gap via Computation Chain
- LecturerProf. Hung-Wei Tseng (University of California, Riverside))
Host: Kai-Min Chung - Time2026-05-26 (Tue.) 15:00 ~ 17:00
- LocationAuditorium 101 at IIS new Building
Abstract
Fully homomorphic encryption (FHE) offers a powerful paradigm for privacy-preserving computing, enabling third parties to process data without ever decrypting it. However, massive ciphertext expansion, costly bootstrapping processes for noise reduction, and severe computational overhead have rendered FHE largely impractical for decades.
Fortunately, recent advancements in approximate arithmetic, hardware-accelerated bootstrapping, and parallelized HE algorithms are steadily closing the performance gap between FHE and plaintext computation. While much of the field currently focuses on local optimizations—such as accelerating specific operations or addressing isolated bottlenecks—FHE applications can achieve significantly higher efficiency if computation chains are optimized at a broader, system-level scope.
In this talk, Hung-Wei will present three concepts that make FHE more feasible for real-world applications:
- Reducing storage and data-exchange overhead via word-level encryption.
- Minimizing transcoding demands by substituting logical operations with approximate arithmetic operations.
- Decreasing bootstrapping frequency through novel code optimization strategies.
To conclude, Hung-Wei will demonstrate how these proposed concepts successfully improve the state-of-the-art performance of FHE-based database queries by at least 20x.
BIO
Hung-Wei Tseng is currently an associate professor in the Department of Electrical and Computer Engineering and a cooperating faculty of the Department of Computer Science and Engineering at University of California, Riverside.
Hung-Wei is interested in designing architecture, programming language frameworks, and system infrastructures that allow applications and programmers to use modern heterogeneous hardware components more efficiently. Hung-Wei's recent focus is on using hardware accelerators (e.g., TPU, Ray Tracing) to improve application performance and privacy-perserving computing through more efficient homomorphic encryptions.