NBsyxx / privacy-preserving-machine-learning-with-hormomophic-encrypted-data
This project studied homomorphic encryption and attempted to apply it in training machine learning models. We trained some models on plain data and evaluated them on encrypted data using encrypted parameters. We eventually created a logistic regression model that trains on encrypted data and maintains high accuracy
☆13Updated 2 years ago
Alternatives and similar repositories for privacy-preserving-machine-learning-with-hormomophic-encrypted-data:
Users that are interested in privacy-preserving-machine-learning-with-hormomophic-encrypted-data are comparing it to the libraries listed below
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption☆69Updated 4 years ago
- SAFEFL: MPC-friendly Framework for Private and Robust Federated Learning☆37Updated last year
- IEEE TIFS'20: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning☆24Updated 2 years ago
- An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawit…☆85Updated 5 years ago
- personal implementation of secure aggregation protocol☆43Updated last year
- Privacy-Preserving Convolutional Neural Networks using Homomorphic Encryption☆79Updated 9 months ago
- Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning☆27Updated 3 months ago
- Secure Linear Regression in the Semi-Honest Two-Party Setting.☆39Updated 5 years ago
- Paper Notes in MPC with Applications to PPML☆69Updated last year
- Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, …☆60Updated 3 years ago
- Chain-PPFL: A Privacy-Preserving Federated Learning Framework based on Chained SMC☆30Updated 4 years ago
- 基于phe库的安全多方计算协议实现☆12Updated 2 years ago
- MPC Secure Multiparty Computation. A three-party secret-sharing-based vertical federated learning setting. The data are vertically parti…☆23Updated 5 years ago
- A secure aggregation system for private federated learning☆39Updated 11 months ago
- Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradi…☆29Updated 3 years ago
- ☆87Updated 4 years ago
- Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learn- ing…☆21Updated 2 years ago
- Repository for collection of research papers on multi-party learning.☆32Updated last year
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆33Updated 4 years ago
- Homomorphic Encryption and Federated Learning based Privacy-Preserving☆68Updated last year
- Symmetric Homomorphic Encryption☆9Updated 2 years ago
- FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning☆19Updated 2 years ago
- This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)☆21Updated 4 years ago
- Secure Aggregation for FL☆34Updated last year
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆116Updated 6 months ago
- ☆36Updated 5 months ago
- A secure multi-party computation library based on arithmetic secret sharing and function secret sharing.☆27Updated last month
- ☆16Updated 6 months ago
- Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference☆48Updated 4 years ago
- Multi‐key homomorphic encryption based on MKCKKS☆19Updated 2 years ago