privacy
differential-privacy
privacy | differential-privacy | |
---|---|---|
2 | 5 | |
1,874 | 2,980 | |
0.7% | 0.5% | |
7.8 | 1.5 | |
8 days ago | 10 days ago | |
Python | Go | |
Apache License 2.0 | Apache License 2.0 |
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.
privacy
differential-privacy
- Launch HN: Sarus (YC W22) – Work on sensitive data with differential privacy
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Google releases differential privacy pipeline for Python
An example is probably easier :) I quote here the description of the Google's differential privacy example:
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Google AI Releases A New Differentially Private Clustering Algorithm
GitHub: https://github.com/google/differential-privacy/tree/main/learning/clustering
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Wehe – Check Your ISP for Net Neutrality Violations
Maybe it is not so radical. The original, pre-web internet was not client-server. Each end of the connection potentially had something the other wanted. IMO, that's a truer representation of the real world. Today's internet is entirely web and mobile app centric, as if the world is nothing more than a feedlot, with only a small number of large scale "farmers".
https://github.com/google/differential-privacy/blob/main/exa...
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Practical Differential Privacy w/ Apache Beam
One of the most durable techniques to protect user privacy is through differential privacy. In a previous post, we explored how to build an Apache Beam pipeline that extracted and counted ngrams from HackerNews comments. Today, we'll take the same pipeline and upgrade it with some differential privacy goodness using Privacy-on-Beam from Google's differential privacy library.
What are some alternatives?
tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow
fully-homomorphic-encryption - An FHE compiler for C++
dp-xgboost
keepassxc - KeePassXC is a cross-platform community-driven port of the Windows application “Keepass Password Safe”.
EnvisEdge - Deploy recommendation engines with Edge Computing
Differential-Privacy-Guide - Differential Privacy Guide
beamdemos
adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
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.
interpret - Fit interpretable models. Explain blackbox machine learning.