NBsyxx / privacy-preserving-machine-learning-with-hormomophic-encrypted-dataLinks
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
☆14Updated 3 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
Sorting:
- SAFEFL: MPC-friendly Framework for Private and Robust Federated Learning☆46Updated 2 years ago
- Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learn- ing…☆23Updated 3 years ago
- Privacy-Preserving Deep Learning via Additively Homomorphic Encryption☆72Updated 4 years ago
- IEEE TIFS'20: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning☆25Updated 3 years ago
- Code repository for the paper at USENIX Security'24☆34Updated last year
- MPC Secure Multiparty Computation. A three-party secret-sharing-based vertical federated learning setting. The data are vertically parti…☆24Updated 6 years ago
- Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, …☆61Updated 3 years ago
- Privacy-Preserving Convolutional Neural Networks using Homomorphic Encryption☆83Updated last year
- An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawit…☆92Updated 6 years ago
- personal implementation of secure aggregation protocol☆47Updated last year
- Paper Notes in MPC with Applications to PPML☆71Updated last year
- FedAvg code with privacy protection function, the application of Paillier homomorphic encryption algorithm and differential privacy, diff…☆131Updated last year
- Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing☆51Updated 4 years ago
- Repository for collection of research papers on multi-party learning.☆34Updated 2 years ago
- Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradi…☆31Updated 4 years ago
- ☆38Updated last year
- Multi‐key homomorphic encryption based on MKCKKS☆20Updated 3 years ago
- FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning☆20Updated 2 years ago
- Secure Aggregation for FL☆36Updated 2 years ago
- [INFOCOM24' & TDSC25']FedPHE & Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning☆39Updated 3 months ago
- Materials about Privacy-Preserving Machine Learning☆260Updated 2 months ago
- MPC team of Security Group in HITSZ☆19Updated 4 months ago
- Unified, Simplified, Tight and Fast Privacy Amplification in the Shuffle Model of Differential Privacy☆11Updated last year
- A secure multi-party computation library based on arithmetic secret sharing and function secret sharing.☆88Updated last week
- A secure aggregation system for private federated learning☆45Updated last year
- Secure Linear Regression in the Semi-Honest Two-Party Setting.☆37Updated 6 years ago
- Implementation of protocols in Falcon☆95Updated last year
- Cryptographically Secure Aggregation for Federated Learning☆11Updated 2 years ago
- A framework for generic hybrid two-party computation and private inference with neural networks☆33Updated 2 years ago
- A simple Python implementation of a secure aggregation protocole for federated learning.☆37Updated 2 years ago