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MeteorLib:Efficient secure multi-party computing mathematical statistics operator library

Background

Secure multi-party computing can effectively ensure that the multi-party data is available and invisible, which can protect privacy data effectively. However, existing secure computing solutions are expensive and difficult to apply in large data scale.

Introduction

We designed an efficient secure multi-party computing mathematical statistics operator library MeteorLib, which significantly reduces the online overhead of participants. The project design is derived from the paper eprint/2023/100, and the work is based on the project Meteor

  • We design a new and improved 3-party secret sharing scheme based on the linear feature of replication secret sharing, which significantly optimizes the online overhead and the number of communication rounds. And design an efficient basic operators library from basic cryptographic primitives, including linear operations, multiplication, MSB, and multiplexers.
  • We build an efficient and safe mathematical statistics operator library for widely used operators such as maximum, average, matrix multiplication, RELU, etc., and utilize several specific optimizations to improve efficiency.

Table of Contents

Warning

This codebase is released solely as a reference for other developers, as a proof-of-concept, and for benchmarking purposes. In particular, it has not had any security review, has a number of implementational TODOs, has a number of known bugs (especially in the malicious implementation), and thus, should be used at your own risk. You can contribute to this project by creating pull requests and submitting fixes and implementations. The code has not run end-to-end inference and we provide online computation at present, the setup phase will be added in future.

Requirements

  • The code should work on most Linux distributions (It has been developed and tested with Ubuntu 18.04).

  • Required packages for Meteor:

    Install these packages with your favorite package manager, e.g, sudo apt-get install <package-name>.

Source Code

Repository Structure

  • files/ - Shared keys, IP addresses and data files.
  • files/preload - Contains data for pretrained network from SecureML. The other networks can be generated using scripts and functions in secondary.cpp
  • lib_eigen/ - Eigen library for faster matrix multiplication.
  • src/ - Source code.
  • util/ - Dependencies for AES randomness.
  • scripts/ - Contains python code to generate trained models for accuracy testing over a batch.
  • The god script makes remote runs simpler (as well as the makefile)

Building the code

To build MeteorLib, run the following commands:

cd MeteorLib
make all -j$(nproc)

Running the code

To run the code, simply choose one of the following options:

  • make: Prints all the possible makefile options.
  • make terminal: Runs the 3PC code on localhost with output from $P_0$ printed to standard output.
  • make file: : Runs the 3PC code on localhost with output from $P_0$ printed to a file (in output/3PC.txt)
  • make valg: Useful for debugging the code for set faults. Note that the -03 optimization flag needs to be suppressed (toggle lines 42, 43 in makefile)
  • make command: Enables running a specific network, dataset, adversarial model, and run type (localhost/LAN/WAN) specified through the makefile. This takes precedence over choices in the src/main.cpp file.
  • To run the code over tmux over multiple terminals, make zero, make one, and make two come in handy.
  • Finally, the makefile (line 4-15) contains the descriptions of the arguments accepted by the executable.

For questions, please create git issues; for eventual replies, you can also reach out to [email protected]