MPyC supports secure m-party computation tolerating a dishonest minority of up to t passively corrupt parties, where m ≥ 1 and 0 ≤ t < m/2. The underlying cryptographic protocols are based on threshold secret sharing over finite fields (using Shamir's threshold scheme as well as pseudorandom secret sharing).

The details of the secure computation protocols are mostly transparent due to the use of sophisticated operator overloading combined with asynchronous evaluation of the associated protocols.

See the MPyC homepage for more info and background.

Click the "launch binder" badge above to view the entire repository and try out the Jupyter notebooks from the demos directory in the cloud, without any install.

Pure Python, no dependencies.

Run pip install . in the root directory (containing file setup.py).
Or, run pip install -e ., if you want to edit the MPyC source files.

See demos for Python programs and Jupyter notebooks with lots of example code.

See Read the Docs for Sphinx-based documentation, including an overview of the demos.
See GitHub Pages for pydoc-based documentation.

1. Python 3.8+ (following NumPy's deprecation policy).

2. Installing package gmpy2 is optional, but will considerably enhance the performance of mpyc. As of December 12, 2021 with the release of gmpy2 2.1, installation has been simplified greatly: pip install gmpy2 is now supported on all major Linux/MacOS/Windows platforms via prebuilt wheels. If you use the conda package and environment manager, conda install gmpy2 should do the job.

3. Use run-all.sh or run-all.bat in the demos directory to have a quick look at all pure Python demos. Demos bnnmnist.py and cnnmnist.py require NumPy, demo kmsurvival.py requires pandas, Matplotlib, and lifelines, and demo ridgeregression.py (and therefore demo multilateration.py) even require Scikit-learn. Also note the example Linux shell scripts and Windows batch files in the docs and tests directories.

4. Directory demos\.config contains configuration info used to run MPyC with multiple parties. Also, Windows batch file gen.bat shows how to generate fresh key material for SSL. To generate SSL key material of your own, first run pip install cryptography (alternatively, run pip install pyOpenSSL, which will also install the cryptography package).

5. To use the Jupyter notebooks demos\*.ipynb, you need to have Jupyter installed, e.g., using pip install jupyter. An interesting feature of Jupyter is the support of top-level await. For example, instead of mpc.run(mpc.start()) you can simply use await mpc.start() anywhere in a notebook cell, even outside a coroutine.

6. For Python, you also get top-level await by running python -m asyncio to launch a natively async REPL. By running python -m mpyc instead you even get this REPL with the MPyC runtime preloaded!

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