TNO-MPC / communication

TNO PET Lab - secure Multi-Party Computation (MPC) - Communication

TNO PET Lab - secure Multi-Party Computation (MPC) - Communication

The TNO PET Lab consists of generic software components, procedures, and functionalities developed and maintained on a regular basis to facilitate and aid in the development of PET solutions. The lab is a cross-project initiative allowing us to integrate and reuse previously developed PET functionalities to boost the development of new protocols and solutions.

The package tno.mpc.communication is part of the TNO Python Toolbox.

Limitations in (end-)use: the content of this repository may solely be used for applications that comply with international export control laws.
This implementation of cryptographic software has not been audited. Use at your own risk.

Documentation

Documentation of the tno.mpc.communication package can be found here.

Install

Easily install the tno.mpc.communication package using pip:

$ python -m pip install tno.mpc.communication

Note: The package specifies several optional dependency groups:

• gmpy: Adds support for sending various gmpy2 types
• tests: Includes all optional libraries required to run the full test suite
• tls: Required if SSL is needed
• bitarray: Adds support for sending bitarray types
• numpy: Adds support for sending numpy types
• pandas: Adds support for sending pandas types

See sending, receiving messages for more information on the supported third party types. Optional dependencies can be installed by specifying their names in brackets after the package name, e.g. when using pip install, use pip install tno.mpc.communication[extra1,extra2] to install the groups extra1 and extra2.

Usage

The communication module uses async functions for sending and receiving. If you are familiar with the async module, you can skip to the Pools section.

Async explanation

When async functions are called, they return what is called a coroutine. This is a special kind of object, because it is basically a promise that the code will be run and a result will be given when the coroutine is given to a so-called event loop. For example, see the following

import asyncio

async def add(a: int, b: int) -> int:
    return a + b

def main():
    a,b = 1, 2
    coroutine_object = add(a, b) # This is now a coroutine object of type Awaitable[int]
    event_loop = asyncio.get_event_loop() # This is the event loop that will run the coroutine
    result = event_loop.run_until_complete(coroutine_object) # This call starts the coroutine in the event loop
    print(result) # this prints 3

if __name__ == "__main__":
    main()

As you can see from the example, the async methods are defined using async def, which tells python that it should return a coroutine. We saw how we can call an async function from a regular function using the event loop. Note that you should never redefine the event loop and always retrieve the event loop as done in the example (unless you know what you are doing). We can also call async functions from other async functions using the await statement, as is shown in the following example.

import asyncio

async def add_four_numbers(first: int, second: int, third: int, fourth: int) -> int:
    first_second = await add(first, second) # This is blocking, so the function will wait until this is done
    third_fourth_coroutine = add(third, fourth) # This is non-blocking, so the code will continue while the add(third,fourth) code starts running
    # we can do some other stuff here
    print("I am a print statement")
    third_fourth = await third_fourth_coroutine # we wait until the add(third,fourth) is done
    result = await add(first_second, third_fourth)
    # here it is important to use await for the result, because then an integer is produced and given
    # to the return statement instead of a coroutine
    return result

async def add(a: int, b: int) -> int:
    return a + b

def main():
    a, b, c, d = 1, 2, 3, 4
    coroutine_object = add_four_numbers(a, b, c, d) # This is now a coroutine object of type Awaitable[int]
    event_loop = asyncio.get_event_loop() # This is the event loop that will run the coroutine
    result = event_loop.run_until_complete(coroutine_object) # This call starts the coroutine in the event loop
    print(result) # this prints 10

if __name__ == "__main__":
    main()

Note that the type of the coroutine_object in the main function is an Awaitable[int]. This refers to the fact that the result can be awaited (inside an async function) and will return an integer once that is done.

Pools

A Pool represents a network. A Pool contains a server, which listens for incoming messages from other parties in the network, and clients for each other party in the network. These clients are called upon when we want to send or receive messages.

It is also possible to use and initialize the pool without taking care of the event loop yourself, in that case the template below can be ignored and the examples can be used as one would regularly do. (An event loop is however still needed when using the await keyword or when calling an async function.)

Template

Below you can find a template for using Pool. Alternatively, you could create the pool in the main logic and give it as a parameter to the async_main function.

import asyncio

from tno.mpc.communication import Pool

async def async_main():
    pool = Pool()
    # ...

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(async_main())

Pool initialization

The following logic works both in regular functions and async functions.

Without SSL/TLS (do not use in production)

The following snippet will start a HTTP server and define its clients. Clients are configured on both the sending and the receiving side. The sending side needs to know who to send a message to. The receiving side needs to know who it receives a message from for further handling.

By default the Pool object uses the origin IP and port to identify the client. However, a more secure and robust identification through SSL/TLS certificates is also supported and described in section With SSL/TLS (SSL/TLS certificate as client identifier).

from tno.mpc.communication import Pool

pool = Pool()
pool.add_http_server() # default port=80
pool.add_http_client("Client 1", "192.168.0.101") # default port=80
pool.add_http_client("Client 2", "192.168.0.102", port=1234)

With SSL/TLS

A more secure connection can be achieved by using SSL/TLS. A Pool object can be initialized with paths to key, certificate and CA certificate files that are passed as arguments to a ssl.SSLContext object. More information on the expected files can be found in the Pool.__init__ docstring and the ssl documentation.

from tno.mpc.communication import Pool

pool = Pool(key="path/to/keyfile", cert="path/to/certfile", ca_cert="path/to/cafile")
pool.add_http_server() # default port=443
pool.add_http_client("Client 1", "192.168.0.101") # default port=443
pool.add_http_client("Client 2", "192.168.0.102", port=1234)

We do not pose constraints on the certificates that you use in the protocol. However, your organisation most likely poses minimal security requirements on the certificates used. As such we do not advocate a method for generating certificates but rather suggest to contact your system administrator for obtaining certificates.

With SSL/TLS (SSL/TLS certificate as client identifier)

This approach does not use the origin of a message (HTTP request) as identifier of a party, but rather the SSL/TLS certificate of that party. This requires a priori exchange of the certificates, but is more robust to more complex (docker) network stacks, proxies, port forwarding, load balancers, IP spoofing, etc.

More specifically, we assume that a certificate has a unique combination of issuer Common Name and S/N and use these components to create a HTTP client identifier. Our assumption is based on the fact that we trust the issuer (TSL assumption) and that the issuer is supposed to hand out end-user certificates with different serial numbers.

from tno.mpc.communication import Pool

pool = Pool(key="path/to/own/keyfile", cert="path/to/own/certfile", ca_cert="path/to/cafile")
pool.add_http_server() # default port=443
pool.add_http_client("Client 1", "192.168.0.101", port=1234, cert="path/to/client/certfile")

Additional dependencies are required in order to load and compare certificates. These can be installed by installing this package with the tls extra, e.g. pip install tno.mpc.communication[tls].

Adding clients

HTTP clients are identified by an address. The address can be an IP address, but hostnames are also supported. For example, when communicating between two docker containers on the same network, the address that is provided to pool.add_http_client can either be the IP address of the client container or the name of the client container.

Sending, receiving messages

The library supports sending the following objects through the send and receive methods:

• strings
• byte strings
• integers
• floats
• enum (partially, see Serializing Enum)
• (nested) lists/tuples/dictionaries/numpy arrays containing any of the above. Combinations of these as well.

Under the hood ormsgpack is used, additional options can be activated using the option parameter (see, https://github.com/aviramha/ormsgpack#option).

Messages can be sent both synchronously and asynchronously. If you do not know which one to use, use the synchronous methods with await.

# Client 0
await pool.send("Client 1", "Hello!") # Synchronous send message (blocking)
pool.asend("Client 1", "Hello!")      # Asynchronous send message (non-blocking, schedule send task)

# Client 1
res = await pool.recv("Client 0") # Receive message synchronously (blocking)
res = pool.arecv("Client 0")      # Receive message asynchronously (non-blocking, returns Future if message did not arrive yet)

Custom message IDs

# Client 0
await pool.send("Client 1", "Hello!", "Message ID 1")

# Client 1
res = await pool.recv("Client 0", "Message ID 1")

Custom serialization logic

It is also possible to define serialization logic in custom classes and load the logic into the commmunication module. An example is given below. We elaborate on the requirements for such classes after the example.

class SomeClass:

    def serialize(self, **kwargs: Any) -> Dict[str, Any]:
        # serialization logic that returns a dictionary

    @staticmethod
    def deserialize(obj: Dict[str, Any], **kwargs: Any) -> 'SomeClass':
        # deserialization logic that turns the dictionary produced
        # by serialize back into an object of type SomeClass

The class needs to contain a serialize method and a deserialize method. The type annotation is necessary and validated by the communication module. Next to this, the **kwargs argument is also necessary to allow for nested (de)serialization that makes use of additional optional keyword arguments. It is not necessary to use any of these optional keyword arguments. If one does not make use of the **kwargs and also does not make a call to a subsequent Serialization.serialize() or Serialization.deserialize(), it is advised to write **_kwargs: Any instead of **kwargs: Any.

To add this logic to the communication module, you have to run the following command at the start of your script. The check_annotiations parameter determines whether the type hints of the serialization code and the presence of a **kwargs parameter are checked. You should only change this to False if you are exactly sure of what you are doing.

from tno.mpc.communication import Serialization

if __name__ == "__main__":
   Serialization.set_serialization_logic(SomeClass, check_annotations=True)

Serializing Enum

The Serialization module can serialize an Enum member; however, only the value is serialized. The simplest way to work around this limitation is to convert the deserialized object into an Enum member:

from enum import Enum, auto


class TestEnum(Enum):
    A = auto()
    B = auto()

enum_obj = TestEnum.B

# Client 0
await pool.send("Client 1", enum_obj)

# Client 1
res = await pool.recv("Client 0")  # 2 <class 'int'>
enum_res = TestEnum(res)  # TestEnum.B <enum 'TestEnum'>

Example code

Below is a very minimal example of how to use the library. It consists of two instances, Alice and Bob, who greet each other. Here, Alice runs on localhost and uses port 61001 for sending/receiving. Bob also runs on localhost, but uses port 61002.

alice.py

import asyncio

from tno.mpc.communication import Pool


async def async_main():
    # Create the pool for Alice.
    # Alice listens on port 61001 and adds Bob as client.
    pool = Pool()
    pool.add_http_server(addr="127.0.0.1", port=61001)
    pool.add_http_client("Bob", addr="127.0.0.1", port=61002)

    # Alice sends a message to Bob and waits for a reply.
    # She prints the reply and shuts down the pool
    await pool.send("Bob", "Hello Bob! This is Alice speaking.")
    reply = await pool.recv("Bob")
    print(reply)
    await pool.shutdown()


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(async_main())

bob.py

import asyncio

from tno.mpc.communication import Pool


async def async_main():
    # Create the pool for Bob.
    # Bob listens on port 61002 and adds Alice as client.
    pool = Pool()
    pool.add_http_server(addr="127.0.0.1", port=61002)
    pool.add_http_client("Alice", addr="127.0.0.1", port=61001)

    # Bob waits for a message from Alice and prints it.
    # He replies and shuts down his pool instance.
    message = await pool.recv("Alice")
    print(message)
    await pool.send("Alice", "Hello back to you, Alice!")
    await pool.shutdown()


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(async_main())

To run this example, run each of the files in a separate terminal window. Note that if alice.py is started prior to bob.py, it will throw a ClientConnectorError. Namely, Alice tries to send a message to port 61002, which has not been opened by Bob yet. After starting bob.py, the error disappears.

The outputs in the two terminals will be something similar to the following:

>>> python alice.py
2022-07-07 09:36:20,220 - tno.mpc.communication.httphandlers - INFO - Serving on 127.0.0.1:61001
2022-07-07 09:36:20,230 - tno.mpc.communication.httphandlers - INFO - Received message from 127.0.0.1:61002
Hello back to you, Bob!
2022-07-07 09:36:20,232 - tno.mpc.communication.httphandlers - INFO - HTTPServer: Shutting down server task
2022-07-07 09:36:20,232 - tno.mpc.communication.httphandlers - INFO - Server 127.0.0.1:61001 shutdown

>>> python bob.py
2022-07-07 09:36:16,915 - tno.mpc.communication.httphandlers - INFO - Serving on 127.0.0.1:61002
2022-07-07 09:36:20,223 - tno.mpc.communication.httphandlers - INFO - Received message from 127.0.0.1:61001
Hello Bob! This is Alice speaking.
2022-07-07 09:36:20,232 - tno.mpc.communication.httphandlers - INFO - HTTPServer: Shutting down server task
2022-07-07 09:36:20,256 - tno.mpc.communication.httphandlers - INFO - Server 127.0.0.1:61002 shutdown

Test fixtures

The tno.mpc.communication package exports several pytest fixtures as pytest plugins to facilitate the user in testing with pool objects. The fixtures take care of all configuration and clean-up of the pool objects so that you don't have to worry about that.

Usage:

# test_my_module.py
import pytest
from typing import Callable
from tno.mpc.communication import Pool

def test_with_two_pools(http_pool_duo: tuple[Pool, Pool]) -> None:
    sender, receiver = http_pool_duo
    # ... your code

def test_with_three_pools(http_pool_trio: tuple[Pool, Pool, Pool]) -> None:
    alice, bob, charlie = http_pool_trio
    # ... your code

@pytest.mark.parameterize("n_players", (2, 3, 4))
def test_with_variable_pools(
    n_players: int,
    http_pool_group_factory: Callable[[int], tuple[Pool, ...]],
) -> None:
    pools = http_pool_group_factory(n_players)
    # ... your code

Fixture scope

The scope of the fixtures can be set dynamically through the --fixture-pool-scope option to pytest. Note that this will also change the scope of the global event_loop fixture that is provided by pytest-asyncio. By default, in line with pytest_asyncio, the scope of all our fixtures is "function". We advise to configure a larger scope (e.g. "session", "package" or "module") when possible to reduce test set-up and teardown time.

Our fixtures pass True to the port_reuse argument of aiohttp.web.TCPSite. Their documentation states that this option is not supported on Windows (outside of WSL). If you experience any issues, please disable the plugin by adding -p no:pytest_tno.tno.mpc.communication.pytest_pool_fixtures to your pytest configuration. Note that without port_reuse the tests may crash, as the test may try to bind to ports which may not have been freed by the operating system. For more reliable testing, run the tests on a WSL / Linux platform.