There are 19 repositories under privacy-preserving topic.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
**Old repository, check GitHub organization**. Tenacity is an easy-to-use, privacy-friendly, FLOSS, cross-platform multi-track audio editor/recorder for Windows, macOS, Linux and other operating systems.
An Industrial Grade Federated Learning Framework
A unified framework for privacy-preserving data analysis and machine learning
基于区块链的符合W3C DID和Verifiable Credential规范的分布式身份解决方案
Golang implementation of the PlatON protocol
Windows 11 Guide. Though, most of the Tools, Programs, Resources will also work for Windows 10.
Open source privacy-friendly analytics
nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph
Linux Guide. Learn about Linux Hardware vendors, Linux in the Cloud, Desktop Environments, Window Mangers, Linux Distributions, Linux Security, Graphics (AMD/NVIDIA/Intel ARC), and Software Apps.
Privacy preserving voluntary Covid-19 self-reporting platform. Share your location history and status, get alerts you are in high risk areas and identify high risk regions
Differential private machine learning
Decentralized & federated privacy-preserving ML training, using p2p networking, in JS
zkChannels: Anonymous Payment Channels for Bitcoin, Zcash, Tezos and more
SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
Privacy -preserving Neural Networks
Python implementation of anonymous linkage using cryptographic linkage keys
MixEth: efficient, trustless coin mixing service for Ethereum
A privacy-preserving app for comparing last-known locations of coronavirus patients
Curated list of awesome browser extensions that protect your privacy
mirror of https://mypdns.org/my-privacy-dns/matrix as it is obviously no longer safe to do Girhub nor have we no longer any trust in them. See https://mypdns.org/my-privacy-dns/porn-records/-/issues/1347
Get usage metrics and crash reports for your API, library, or command line tool.
a recommender engine node-js package for general use and easy to integrate.
Detecting skin cancer in encrypted images with TensorFlow
(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare
A client side tier list maker, without any ads
Anonymizing Library for Apache Spark
personal implementation of secure aggregation protocol
A C++ Implementation of Short Randomizable Signatures (PS Signatures) and EL PASSO (Privacy-preserving, Asynchronous Single Sign-On)
This is a proof-of-concept implementation of the framework proposed by [Alves and Aranha 2018] with the purpose of offering a wrapper on MongoDB's Python driver that enables a application to store and query encrypted data on the database.
a mini music player, highly compatible & works
A decentralised alternative to DocuSign on Ethereum network.
Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning scheme called PFLM. PFLM releases the assumption of threshold while maintaining the security guarantees. Additionally, we design a result verification algorithm based on a variant of ElGamal encryption to verify the correctness of aggregated results from the cloud server. The verification algorithm is integrated into PFLM as a part. Security analysis in a random oracle model shows that PFLM guarantees privacy against active adversaries. The implementation of PFLM and experiments demonstrate the performance of PFLM in terms of computation and communication.
📊 Privacy Preserving Medical Data Analytics using Secure Multi Party Computation. An End-To-End Use Case. A. Giannopoulos, D. Mouris M.Sc. thesis at the University of Athens, Greece.
Ma liste de paramètres à modifier dans le menu about:config de Firefox