concrete-numpy
sspipe
| concrete-numpy | sspipe | |
|---|---|---|
| 4 | 1 | |
| 226 | 145 | |
| - | 0.0% | |
| 4.7 | 0.0 | |
| 8 days ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
concrete-numpy
-
[P] ML over Encrypted Data
Hi everyone, we have developed a library that applies numpy functions over encrypted data (using homomorphic encryption). The repo is available in open source at https://github.com/zama-ai/concrete-numpy
- Compile NumPy Functions to Their Fully Homomorphic Encryption (FHE) Equivalents
- Concrete Numpy: compile various Numpy functions into their Fully Homomorphic Encryption (#FHE) equivalents.
- Concrete-Numpy: Data Science and Machine Learning over encrypted data.
sspipe
What are some alternatives?
concrete-ml - Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
siuba - Python library for using dplyr like syntax with pandas and SQL
mlcourse.ai - Open Machine Learning Course
tsflex - Flexible time series feature extraction & processing
liberate-fhe - A Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.
kindleServer - This project serve HTML files (and a few more) saved in your computer with a UI suitable for Kindle web browser. On top of that, it include a Read Mode (thanks to ReadabiliPy) to display the text in a comfortable size without have to use the 'Article Mode' in Kindle web browser.
python-fhez
seaborn - Statistical data visualization in Python
heflow - Open source platform for the privacy-preserving machine learning lifecycle
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Dask - Parallel computing with task scheduling