fully-homomorphic-encryption
differential-privacy
fully-homomorphic-encryption | differential-privacy | |
---|---|---|
19 | 5 | |
3,455 | 2,981 | |
0.3% | 0.5% | |
7.0 | 1.5 | |
about 2 months ago | 7 days ago | |
C++ | 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.
fully-homomorphic-encryption
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What are the current hot topics in type theory and static analysis?
Secure computing. This includes Fully Homomorphic Encryption AKA FHE, of which there is a language/compiler which just got released and Google's older FHE compiler. FHE is probably more "compiler" than "type system", e.g. Google's compiler works on C++. Also Security Type Systems which include Oblivious data structures and Oblivious ADTs.
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Ask HN: Should we follow what impresses us?
I don't have any advice for you, but I do work on homomorphic encryption at Google and we have an FHE compiler project [1] (though it is likely going to change a lot in the coming year). I happen to have a math PhD, so the transition to this field was not a huge stretch, but before that I worked in supply chain optimization for data centers, and just decided this was too exciting to pass up.
[1]: https://github.com/google/fully-homomorphic-encryption/issue...
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Spiral’s Homomorphic Encryption – Is This the Future of Privacy?
+1, and some compilers already exist to do that for you. See, e.g., Google's compiler (which I work on). https://github.com/google/fully-homomorphic-encryption
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We’re Christian Mouchet, Jean-Philippe Bossuat, Kurt Rohloff, Nigel Smart, Pascal Paillier, Rand Hindi, Wonkyung Jung, various researchers and library developers of homomorphic encryption to answer questions about homomorphic encryption and why it’s important for the future of data privacy! AMA
Once the tools are written, you should be able to take a program written in some language foo and transpile it to a FHE version of foo. See Google's C++ to FHE-C++ transpiler. Thus, you can test/debug your application without FHE before transpiling to something that is FHE.
- Google releases C++ Transpiler for Fully Homomorphic Encryption
- Fully Homomorphic Encryption by Google
- Fully homomorphic encryption (Google GitHub)
- r/crypto - Fully Homomorphic Encryption by Google
- Fully Homomorphic Encryption (FHE)
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?
SEAL - Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library.
privacy - Library for training machine learning models with privacy for training data
i2pd - 🛡 I2P: End-to-End encrypted and anonymous Internet
keepassxc - KeePassXC is a cross-platform community-driven port of the Windows application “Keepass Password Safe”.
monero - Monero: the secure, private, untraceable cryptocurrency
dp-xgboost
HElib - HElib is an open-source software library that implements homomorphic encryption. It supports the BGV scheme with bootstrapping and the Approximate Number CKKS scheme. HElib also includes optimizations for efficient homomorphic evaluation, focusing on effective use of ciphertext packing techniques and on the Gentry-Halevi-Smart optimizations.
beamdemos
EVA - Compiler for the SEAL homomorphic encryption library
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.
libaco - A blazing fast and lightweight C asymmetric coroutine library 💎 ⛅🚀⛅🌞
interpret - Fit interpretable models. Explain blackbox machine learning.