mpy

mpyc

MPyC for Secure Multiparty Computation in Python

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MPyC MPyC logo Secure Multiparty Computation in Python

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.

Installation:

Just run: python setup.py install (pure Python, no dependencies).

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, and GitHub Pages for pydoc-based documentation.

Notes:

  1. Python 3.6+ (Python 3.5 or lower is not sufficient).

  2. Installing package gmpy2 is optional, but will considerably enhance the performance of mpyc. If you use the conda package and environment manager, conda install gmpy2 should do the job. Otherwise, pip install gmpy2 can be used on Linux (first running apt install libmpc-dev may be necessary too), but on Windows, this may fail with compiler errors. Fortunately, ready-to-go Python wheels for gmpy2 can be downloaded from Christoph Gohlke's excellent Unofficial Windows Binaries for Python Extension Packages webpage. Use, for example, pip install gmpy2-2.0.8-cp39-cp39-win_amd64.whl to finish installation.

  3. Use run-all.sh or run-all.bat in the demos directory to have a quick look at all pure Python demos. The demos bnnmnist.py and cnnmnist.py require NumPy, the demo kmsurvival.py requires pandas, Matplotlib, and lifelines, and the demo ridgeregression.py even requires 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. OpenSSL is required to generate SSL key material of your own, use pip install pyOpenSSL.

  5. To use the Jupyter notebooks demos\*.ipynb, you need to have Jupyter installed, e.g., using pip install jupyter. The latest version of Jupyter will come with IPython 7.x, which supports top-level await. For example, instead of mpc.run(mpc.start()) one can now simply write await mpc.start() anywhere in a notebook cell, even outside a coroutine.

  6. For Python 3.8+, 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!

Copyright © 2018-2021 Berry Schoenmakers

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