Lowering the barrier of entry to privacy preserving technology
Contributor: Rasswanth S
FALCON is the current state-of-the-art Multiparty Computation (MPC) Framework for Private-Deep Learning(PDL).MPC allows mutually distrusting parties...
Contributor: Param Mirani
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...
Contributor: Aditi Verma
A project to integrate NumPy, the leading package for scientific computations, into PySyft, a library for answering questions with data you cannot...
Contributor: arpitvaghela
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....
Contributor: vsquareg
This project aims at firstly, investigating the performance of the current implementations against its alternatives at various levels of data...
Contributor: 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...
Contributor: ArchitG
Zero-knowledge proofs have an important role to play in the future of verified machine learning prediction. However, no deep learning framework has...
Contributor: 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...
Contributor: Ravikant Singh
FV (Fan-Vercauteren) Homomorphic Encryption scheme is one of the leading approaches in homomorphic encryption. Homomorphic encryption is a form of...
