MPyC supports secure m-party computation tolerating a dishonest minority of up to t passively corrupt parties, where m ≥ 1 and 0 ≤ t ≤ (m-1)/2. The underlying 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 MPyC homepage for more info and background.
python setup.py install
python setup.py install --user
demos for usage examples.
Python 3.6+ (Python 3.5 or lower is not sufficient).
gmpy2is optional, but will considerably enhance the performance of
mpyc. On Linux,
pip install gmpy2should do the job, but on Windows, this may fail with compiler errors. Fortunately, ready-to-go Python wheels for
gmpy2can be downloaded from Christoph Gohlke's excellent Unofficial Windows Binaries for Python Extension Packages webpage. Use, for example,
pip install gmpy2-2.0.8-cp36-cp36m-win_amd64.whlto finish installation.
A few simple Windows batch files are provided in the
demosdirectory. Also note the Windows batch files in the
demos\.configcontains configuration info and key material needed to run MPyC with multiple parties. Windows batch file 'gen.bat' shows how to generate fresh key material for pseudorandom secret sharing and SSL. OpenSSL is required to generate SSL key material of your own, use
pip install pyOpenSSL.
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 7.0+, which supports top-level
await. Instead of
mpc.run(mpc.start())one can now simply write
await mpc.start()anywhere in a notebook cell, even outside a coroutine.
Copyright © 2018-2019 Berry Schoenmakers