Package: tinynmc
Minimal pure-Python implementation of a secure multi-party computation (MPC)protocol for evaluating arithmetic sum-of-products expressionsvia a non-interactive computation phase.Installation and UsageThis library is available as apackage on PyPI:python-mpipinstalltinynmcThe library can be imported in the usual way:importtinynmcfromtinynmcimport*Basic ExampleThis example involves three contributorsa,b, andc(parties submitting private input values) and three nodes0,1, and2(parties performing a computation):>>>nodes=[node(),node(),node()]The overall sum-of-products expression being computed is(1 * 2 * 3) + (4 * 5). First, the contributors agree on a workflow signature. The signature lists the number of factors in each term:>>>signature=[3,2]The signature must be shared with every node so that the nodes can collectively perform the preprocessing phase; this can be accomplished using any MPC protocol that supports multiplication of secret-shared values, such as theSPDZprotocol (a similarly simple implementation of which can be seen in theTinySMPClibrary):>>>preprocess(signature,nodes)Next, each factor in the workflow is contributed by one of three contributorsa,b, orc, with the ownership pattern(a * b * c) + (a * b). Each factor is referenced by the contributors according to its(term_index, factor_index)coordinate within the overall expression:((0, 0) * (0, 1)) + ((1, 0) * (1, 1) * (1, 2)).Each contributor can convert its coordinate-value pairs into masked factors by (1) requesting the multiplicative shares of the masks for each coordinate, and (2) masking its factors at each coordinate using those masks:>>>coords_to_values_a={(0,0):1,(1,0):4}>>>masks_from_nodes_a=[node.masks(coords_to_values_a.keys())fornodeinnodes]>>>masked_factors_a=masked_factors(coords_to_values_a,masks_from_nodes_a)>>>coords_to_values_b={(0,1):2,(1,1):5}>>>masks_from_nodes_b=[node.masks(coords_to_values_b.keys())fornodeinnodes]>>>masked_factors_b=masked_factors(coords_to_values_b,masks_from_nodes_b)>>>coords_to_values_c={(0,2):3}>>>masks_from_nodes_c=[node.masks(coords_to_values_c.keys())fornodeinnodes]>>>masked_factors_c=masked_factors(coords_to_values_c,masks_from_nodes_c)Each contributor then broadcasts all of its masked factors to every node, so every node receives all of the masked factors from all of the contributors:>>>broadcast=[masked_factors_a,masked_factors_b,masked_factors_c]Then, every node can locally compute its share of the overall result:>>>result_share_at_node_0=nodes[0].compute(signature,broadcast)>>>result_share_at_node_1=nodes[1].compute(signature,broadcast)>>>result_share_at_node_2=nodes[2].compute(signature,broadcast)Finally, the result can be reconstructed via summation from the result shares received from the nodes:>>>int(sum([result_share_at_node_0,result_share_at_node_1,result_share_at_node_2]))26DevelopmentAll installation and development dependencies are fully specified inpyproject.toml. Theproject.optional-dependenciesobject is used tospecify optional requirementsfor various development tasks. This makes it possible to specify additional options (such asdocs,lint, and so on) when performing installation usingpip:python-mpipinstall.[docs,lint]DocumentationThe documentation can be generated automatically from the source files usingSphinx:python-mpipinstall.[docs]cddocssphinx-apidoc-f-E--templatedir=_templates-o_source..&&makehtmlTesting and ConventionsAll unit tests are executed and their coverage is measured when usingpytest(see thepyproject.tomlfile for configuration details):python-mpipinstall.[test]python-mpytestAlternatively, all unit tests are included in the module itself and can be executed usingdoctest:pythonsrc/tinynmc/tinynmc.py-vStyle conventions are enforced usingPylint:python-mpipinstall.[lint]python-mpylintsrc/tinynmcContributionsIn order to contribute to the source code, open an issue or submit a pull request on theGitHub pagefor this library.VersioningThe version number format for this library and the changes to the library associated with version number increments conform withSemantic Versioning 2.0.0.PublishingThis library can be published as apackage on PyPIby a package maintainer. First, install the dependencies required for packaging and publishing:python-mpipinstall.[publish]Ensure that the correct version number appears inpyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has anautomation rulethat activates and sets as the default all tagged versions. Create and push a tag for this version (replacing?.?.?with the version number):gittag?.?.?gitpushorigin?.?.?Remove any old build/distribution files. Then, package the source into a distribution archive:rm-rfbuilddistsrc/*.egg-infopython-mbuild--sdist--wheel.Finally, upload the package distribution archive toPyPI:python-mtwineuploaddist/*