Seceum / SeceumFL
SeceumFL 联邦学习系统v3.2版本是神谱科技（上海）有限公司基于FATE进行开发的联邦学习产品， SeceumFL具备了安全隔离域、可信计算、联邦建模等技术能力，有效保障数据安全和用户隐私，支持多方数据安全融合及建模计算，提供从模型训练、评估到应用部署的全流程服务。
☆20Updated last year
Related projects ⓘ
Alternatives and complementary repositories for SeceumFL
- 基于同态加密的联邦学习安全聚合系统☆36Updated 3 years ago
- SecMML (Queqiao): Secure MPC (multi-party computation) Machine Learning Framework.☆111Updated last year
- 自己的个人毕设，联邦学习相关，主要是多方安全计算中秘密分享的一种算法实现，以完成不需要第三方的安全隐私传输。（An algorithm implementation of secret sharing in multi-party secure computing to c…☆16Updated 2 years ago
- Paper Notes in MPC with Applications to PPML☆65Updated 7 months ago
- MPC team of Security Group in HITSZ☆19Updated 2 months ago
- 基于phe库的安全多方计算协议实现☆12Updated 2 years ago
- 基于可编程不经意伪随机数的多方隐私求交算法库 Programmable Oblivious PRF & multi-party PSI☆24Updated 2 years ago
- Private set intersection implemented in Python☆100Updated 2 years ago
- Repository for collection of research papers on multi-party learning.☆31Updated last year
- 基于全同态加密的安全人脸识别系统/Secure Face Recognition System based on Fully Homomorphic Encryption☆134Updated last year
- 多方安全计算技术&联邦学习相关/Multi party security computing technology federated learning☆17Updated 9 months ago
- 隐私计算 Hackathon | Data Privacy Protect Hackathon website☆25Updated last year
- HEhub is a library for homomorphic encryption and its applications, and is part of the PrimiHub project.☆90Updated last year
- ☆110Updated 8 months ago
- 基于安全多方计算的隐私保护系统设计与实现☆43Updated 2 years ago
- vertical federated learning demo with crypten☆45Updated 3 years ago
- WeFe ( WeLab Federated Learning ) 是 Welab 汇立集团子公司[天冕科技](https://www.tianmiantech.com )发起的开源项目，为联邦学习生态系统提供了一套好用的可靠的企业级联邦学习平台。☆136Updated 2 months ago
- ☆20Updated 5 years ago
- Secure Linear Regression in the Semi-Honest Two-Party Setting.☆38Updated 5 years ago
- A mathematical and code introduction to the BFV Homomorphic Encryption scheme.☆36Updated 2 years ago
- The Description of the project you can watch my blog☆33Updated 3 years ago
- Multi-party Private Set Intersections & Threshold Set Intersections☆11Updated 3 years ago
- FudanMPL 2.0, a series of multi-party learning frameworks, with rich features, including secure and fast XGBoost, secure Fine-tuning for …☆46Updated 2 months ago
- pMPL: A Robust Multi-Party Learning Framework with a Privileged Party. This project is connected with the publication @ ACM CCS 2022.☆17Updated 3 months ago
- 这是一个基于Paillier同态加密系统的隐私保护SVM模型训练算法。☆13Updated 2 years ago
- 联邦学习可视化☆13Updated 3 years ago
- ☆213Updated 3 months ago
- FedAI 社区中文文档☆131Updated 6 months ago
- Fast Privacy-Preserving Machine Learning on the GPU☆78Updated 3 years ago
- 对称可搜索加密，Cash的实现方案☆16Updated 6 years ago