Differential-Privacy-Guide vs privacy

Differential-Privacy-Guide

Differential Privacy Guide (by mikeroyal)

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privacy

Library for training machine learning models with privacy for training data (by tensorflow)

Machine Learning Privacy

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Differential-Privacy-Guide		privacy
	Project
1	Mentions	2
13	Stars	1,874
-	Growth	0.7%
2.6	Activity	7.8
over 2 years ago	Latest Commit	7 days ago
Python	Language	Python
-	License	Apache License 2.0

The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Differential-Privacy-Guide

Posts with mentions or reviews of Differential-Privacy-Guide. We have used some of these posts to build our list of alternatives and similar projects.

Useful Tools and Programs list for Differential Privacy
1 project | /r/differentialprivacy | 20 Mar 2022

privacy

Posts with mentions or reviews of privacy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-17.

Trasformare dataset tensorflow in array numpy fa perdere accuracy
1 project | /r/ItalyInformatica | 27 Jul 2022
Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
4 projects | news.ycombinator.com | 17 Mar 2022

What are some alternatives?

When comparing Differential-Privacy-Guide and privacy you can also consider the following projects:

adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

differential-privacy - Google's differential privacy libraries.

tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow

gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.

dp-xgboost

ReTube - ReImagine Tubing

EnvisEdge - Deploy recommendation engines with Edge Computing

isp-data-pollution - ISP Data Pollution to Protect Private Browsing History with Obfuscation

tf2-published-models - Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.