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(This is the original version of PySNARK. A rewrite with extra functionality is available here.)

PySNARK is a Python-based system for easily performing verifiable computations based on the Pinocchio zk-SNARK system and the Geppetri extension for proofs on authenticated data. Verifiable computations can also be automatically turned into Solidity smart contracts for use on the Ethereum blockchain.

PySNARK may be used for non-commercial, experimental and research purposes; see for details.

PySNARK is experimental and not fit for production environment. In particular, PySNARK does not use cryptographically secure randomness! See base.cpp and modp.cpp of qaptools.


Unix (Linux/MacOS/...)

First, download the PySNARK dependencies, ate-pairing and xbyak:

git submodule init
git submodule update

Build the ate-pairing library:

cd qaptools/ate-pairing

Build the qaptools library:

cd ../..
cd qaptools

Build and install the pysnark library:

cd ..
python install


PySNARK comes with precompiled Windows executables of qaptools, meaning it is possible to build an install PySNARK by just running

python install

To recompiling qaptools from source, set up a Unix-like build environment such as Mingw with MSYS and use the Unix instructions above.

Using without installation

It is also possible to run PySNARK applications without installing PySNARK. For this, follow the above steps but run python build instead of python install. This makes sure all files are compiled and put in their correct locations. Then, run the application with the PYTHONPATH environment variable set to the PySNARK library, e.g.:

PYTHONPATH=/path/to/pysnark/sources python

The PySNARK toolchain

We discuss the usage of the PySNARK toolchain based on running one of the provided examples acting as each of the different types of parties in a verifiable computation: trusted party, prover, or verifier.

As trusted party

To try out running PySNARK as trusted party performing key generation, do the following:

cd examples
python 3

If PySNARK has been correctly installed, this will perform a verifiable computation that will compute the cube of the input value, 3. At the same time, it will generate all key material needed to verifiably perform the computation in the script. (Performing an example computation is the only way to generate this key material.) PySNARK produces the following files:

  • Files that should be kept secret by the trusted party generating the key material:
    • pysnark_mastersk: zk-SNARK master secret key
  • Files that the trusted party should distribute to provers along with the Python script (i.e., in this case):
    • pysnark_schedule: schedule of functions called in the computation
    • pysnark_masterek: master evaluation key
    • pysnark_ek_main: zk-SNARK evaluation key for the main function of the computation
    • pysnark_eqs_main: equations for the main function of the computation
  • Files that the trusted party should distribute to verifiers:
    • pysnark_schedule: schedule of functions called in the computation
    • pysnark_masterpk: master public key
    • pysnark_vk_main: verificaiton key for the main function
  • Files that the prover should distribute to verifiers:
    • pysnark_proof: proof that the particular computation was performed correctly
    • pysnark_values: input/output values of the computation
  • Files that are not needed anymore after the execution:
    • pysnark_eqs: equations for the zk-SNARK
    • pysnark_wires: wire values of the computation

As prover

To try out running PySNARK as a prover, put the files discussed above (i.e., pysnark_schedule, pysnark_masterek, pysnark_ek_main, and pysnark_eqs_main) together with in a directory and run the same command:

cd examples
python 3

This will perform a verifiable computation based on the previously generated key material.

As verifier

To try out running PySNARK as a verifier, put the files discussed above (i.e., pysnark_schedule, pysnark_masterpk and pysnark_vk_main received from the trusted party, and pysnark_proof and pysnark_values received from the prover) in a folder and run

python -m pysnark.qaptools.runqapver

This will verify the computation proof with respect to the input/output values from the pysnark_values file, e.g,:

# PySNARK i/o
main/o_in: 21
main/o_out: 9261

In this case, we have verifiably computed the fact that the cube of 21 is 9261. See the examples folder for additional examples.

Using commitments

PySNARK allows proofs to refer to committed data using Geppetri. This has three applications:

  • it allows proofs to refer to external private inputs from parties other than the trusted third party;
  • it allows different verifiable computations to share secret data with each other; and
  • it allows to divide a verifiable computation into multiple subcomputations, each with their own evaluation and verification keys (but all based on the same master secret key)

All computations sharing committe data should use the same master secret key.

See examples/ for examples.

External secret inputs

To commit to data, use pysnark.qaptools.runqapinput, e.g., to commit to values 1, 2, and 3 using a commitment named test, use:

python -m pysnark.qaptools.runqapinput test 1 2 3

Alternatively, use pysnark.qaptools.runqapinput.gencomm from a Python script. Share pysnark_wires_test with any prover who wants to perform a computation with respect to this committed data, and pysnark_comm_test to any verifier.

Import this data into the verifiable computation with

[one,two,three] = pysnark.runtime.importcomm("test")

Sharing data between verifiable computations

In the first computation, do

pysnark.runtime.exportcomm([Var(1),Var(2),Var(3)], "test")

and share pysnark_wires_test and pysnark_comm_test with the other prover and the verifier, respectively.

In the second verifiable computation, do

[one,two,three] = pysnark.runtime.importcomm("test")

Sharing data between different parts of a verifiable computation

This is implicitly used whenever a function is called that is decorated with @pysnark.runtime.subqap. When a particular functon is used multiple times in a verifiable computation, using @pysnark.runtime.subqap prevents the circuit for the function to be replicated, resulting in smaller key material (but slower verification).

Using PySNARK for smart contracts

PySNARK supports the automatic generation of smart contracts that verify the correctness of the given zk-SNARK. These smart contracts are written in Solidity and require support for the recent zkSNARK verification opcodes (EIP 196, EIP 197) included in Ethereum Byzantium. To test them out, install a development version of Truffle using these instructions. This functionality is based on ideas from ZoKrates.

Continuing the above example, suppose you have a verifiable computation proof as produced above (i.e., performing runqapver as described above works). First run

truffle init

to initialise Truffle (to just see the Solidity code without installing Truffle, create two empty directories contracts and test). Next, run

python -m pysnark.contract

to generate smart contract contracts/Pysnark.sol to verify computations of the script (using library contracts/Pairing.sol that is also copied into the directory), and test script test/TestPysnark.sol that gives a test case for the contract based on the current I/O and proof. Finally, run

truffle test

to run the test script and check that the given proof can indeed be verified in Solidity.

Note that test/TestPysnark.sol indeed contains the I/O from the computation:

pragma solidity ^0.4.2;

import "truffle/Assert.sol";
import "../contracts/Pysnark.sol";

contract TestPysnark {
    function testVerifies() public {
        Pysnark ps = new Pysnark();
        uint[] memory proof = new uint[](22);
        uint[] memory io = new uint[](2);
        proof[0] = ...;
        proof[21] = ...;
        io[0] = 21; // main/o_in
        io[1] = 9261; // main/o_out
        Assert.equal(ps.verify(proof, io), true, "Proof should verify");

Smart contracts can also refer to commitments, e.g., as imported with the pysnark.runtime.importcomm API call. In this case, the commitment becomes an argument to the verification function (a six-valued integer array), and the test case shows how the commitment used in the present computation should be used as value for that argument, e.g.:

pragma solidity ^0.4.2;

import "truffle/Assert.sol";
import "../contracts/Pysnark.sol";

contract TestPysnark {
    function testVerifies() public {
        Pysnark ps = new Pysnark(); 
        uint[] memory pysnark_comm_test = new uint[](6);
        pysnark_comm_test[0] = ...;
        Assert.equal(ps.verify(proof, io, pysnark_comm_test), true, "Proof should verify");

To get more detailed information about the gas usage of the smart contract, run with Ganache: start ganache-cli; edit truffle.js to add a development network, e.g.:

module.exports = {
  networks: {
    development: {
      host: "",
      port: 8545,
      network_id: "*" // Match any network id

and finally, run truffle test --network development.


To generate PySNARK's documentation, do:

cd docs
make html

Then, open docs/_build/html/index.html.

A compiled PDF of the documentation (generated with make pdf) is available as docs/PySNARK.pdf but this file may not always be up-to-date.


This work is part of the SODA project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731583.


Python-based system for zk-SNARK based verifiable computations and smart contracts




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