tf-encrypted
mia
| tf-encrypted | mia | |
|---|---|---|
| 2 | 1 | |
| 1,192 | 110 | |
| 0.5% | - | |
| 0.0 | 0.0 | |
| 7 months ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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tf-encrypted
- What are the technologies to keep the data secure when it's aggregated from multiple sources?
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Facebook is reportedly trying to analyze encrypted data without deciphering it
There is a TF package for learning on encrypted data. Not familiar with the details or encryption algos supported.
https://github.com/tf-encrypted/tf-encrypted
mia
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I want a reference to a real industry grade ML project with good structure & code.
Projects on privacy in machine learning is what a lot of companies are moving towards. Here’s an end to end project by google called ‘Membership Inference Attacks’ (around 2017) that was one of the first papers on how someone can extract information about people from trained models - https://github.com/spring-epfl/mia Of course Membership Inference Attacks have come a long way since then, but this repository shows you how you can create datasets and train models for such attacks. It’s a really good project but can also be very challenging!
What are some alternatives?
stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
fawkes - Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]
adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Differential-Privacy-Guide - Differential Privacy Guide
openfl - The Open Flash Library for creative expression on the web, desktop, mobile and consoles.
openfl - An open framework for Federated Learning.
privacy - Library for training machine learning models with privacy for training data
garbled-circuit - A two-party secure function evaluation using Yao's garbled circuit protocol