[Nov 7, 2025] Professor Hyoungshick Kim’s seclab, wins Best Poster Award at ACM CCS 2025
- SKKU National Program of Excellence in Software
- Hit310
- 2025-11-07
The paper "Poster: Scalable Privacy-Preserving Linear Regression Training via Homomorphic Encryption", conducted by undergraduate student Yena Cho and Professor Hyoungshick Kim from the Security Engineering Laboratory (Advisor: Hyoungshick Kim, https://seclab.skku.edu), received the Best Poster Award at the ACM Conference on Computer and Communications Security (CCS 2025), one of the most prestigious conferences in the field of security. (Selected 2 out of 41 posters, approximately 4.9%.)
This research proposes a novel protocol that enables efficient linear regression training within an encrypted data environment. The team developed a CKKS-based PP-LR (Privacy-Preserving Linear Regression) protocol to address the high computational cost issue inherent in traditional homomorphic encryption-based training methods. PP-LR efficiently performs gradient descent on encrypted data through feature-level parallelization and a conditional bootstrapping technique. As a result, it achieves up to 15.7× faster training speed compared to existing homomorphic encryption implementations while maintaining an accuracy gap within 0.2% of plaintext training models.