{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cat-a-gpu-accelerated-fhe-framework-with-its","title":"CAT: A GPU-Accelerated FHE Framework with Its Application to High-Precision Private Dataset Query","arxiv_id":"2503.22227","date":"2025-03-28","proceeding":null,"authors":["Qirui Li","Rui Zong"],"abstract":"We introduce an open-source GPU-accelerated fully homomorphic encryption (FHE) framework CAT, which surpasses existing solutions in functionality and efficiency. \\emph{CAT} features a three-layer architecture: a foundation of core math, a bridge of pre-computed elements and combined operations, and an API-accessible layer of FHE operators. It utilizes techniques such as parallel executed operations, well-defined layout patterns of cipher data, kernel fusion/segmentation, and dual GPU pools to enhance the overall execution efficiency. In addition, a memory management mechanism ensures server-side suitability and prevents data leakage. Based on our framework, we implement three widely used FHE schemes: CKKS, BFV, and BGV. The results show that our implementation on Nvidia 4090 can achieve up to 2173$\\times$ speedup over CPU implementation and 1.25$\\times$ over state-of-the-art GPU acceleration work for specific operations. What's more, we offer a scenario validation with CKKS-based Privacy Database Queries, achieving a 33$\\times$ speedup over its CPU counterpart. All query tasks can handle datasets up to $10^3$ rows on a single GPU within 1 second, using 2-5 GB storage. Our implementation has undergone extensive stability testing and can be easily deployed on commercial GPUs. We hope that our work will significantly advance the integration of state-of-the-art FHE algorithms into diverse real-world systems by providing a robust, industry-ready, and open-source tool.","url_abs":"https://arxiv.org/abs/2503.22227v1","url_pdf":"https://arxiv.org/pdf/2503.22227v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"cat-a-gpu-accelerated-fhe-framework-with-its","repo_url":"https://github.com/Rayman96/CAT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}