OpenMined
Lowering the barrier of entry to privacy preserving technology
- 9
- Projects
- 2
- Years accepted
- 5
- In 2021
Projects by year
92021
5 acceptedShow
Implement FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning in SyMPC
Accepted project · by Rasswanth S
FALCON is the current state-of-the-art Multiparty Computation (MPC) Framework for Private-Deep Learning(PDL).MPC allows mutually distrusting parties...
Openmined's Syft library provides an infrastructure for computing on data you do not own and cannot see. It allows data scientists to work with data...
A project to integrate NumPy, the leading package for scientific computations, into PySyft, a library for answering questions with data you cannot...
The Syft ecosystem allows one to write software that can compute over information you do not own on machines you do not have (total) control over....
This project aims at firstly, investigating the performance of the current implementations against its alternatives at various levels of data...
2020
4 acceptedShow
Performing a security audit on privacy-preserving, distributed learning methodologies running on PyGrid with respect to GDPR requirement.
Accepted project · by Afzaal Hussain
In order to bridge the gap between industry and new technology, this project will supply governance models which are relevant to Privacy-Preserving...
Zero-knowledge proofs have an important role to play in the future of verified machine learning prediction. However, no deep learning framework has...
Implement Auto-Scaling of PyGrid servers on Google Cloud
Accepted project · by Anshuman Singh.
The audience of PySyft largely consists of people who would like to train their model on private data that reside on other devices/locations. Right...
Implement Fan-Vercauteren Homomorphic Encryption Scheme in PySyft
Accepted project · by Ravikant Singh
FV (Fan-Vercauteren) Homomorphic Encryption scheme is one of the leading approaches in homomorphic encryption. Homomorphic encryption is a form of...
