Skip to main content

Introduction

MPCStats Demo Infrastructure is designed for privacy-preserving statistical survey using Multi-Party Computation (MPC). It enables multiple data providers to share inputs without exposing them to others while allowing the computation of aggregate statistics. The system is adaptable, supporting various data sources and statistical operations.

Why MPCStats Demo Infrastructure?​

Traditional methods of computing statistics from multiple data sources often require sending raw data to a central server:

  • Privacy Risk: Centralized access to individual data points can expose sensitive information.
  • Input Integrity Concerns: Without verification, data providers could submit incorrect or falsified data.

We provide these features:

  • Data Privacy: Using MPC (built on the MP-SPDZ framework), the system enables computations without exposing individual inputs, ensuring no single party gains access to others’ raw data.
  • Input Validity: Through TLSNotary, data providers can prove their data is authentically sourced from verified websites, preventing falsified inputs.
  • Developer and User Experience:
    • For developers: The infrastructure is easy to set up and modify for various scenarios. While existing frameworks can conduct such surveys, they often require understanding complex underlying technologies and custom implementations. MPCStats provides boilerplate code and a statistical library to simplify customization.
    • For data providers and consumers: By delegating MPC to three non-colluding servers, participants experience a workflow similar to traditional statistical survey systems. This design allows users to submit data and disconnect, enhancing usability without compromising privacy.

MPCStats Demo Infrastructure ensures privacy-preserving and verifiable results, making it suitable for sensitive applications such as financial analysis, health data aggregation, and collaborative statistics.

Demo at Devcon 2024​

We had a demo at Devcon 2024 where participants can prove and share their Binance ETH balance in their spot account using the Binance API. We then derive the mean, median, max, and gini index of the balances of all participants, without revealing their individual balances.

This demo demonstrates how real-world data sources like Binance can be securely integrated into privacy-preserving statistical computations using MPC and TLSNotary. It's a foundational step toward building tools that enable collaborative data analysis while preserving user privacy.

You can explore our implementation code and read our detailed Devcon Demo Report to learn more about the technical details and outcomes of this demonstration. For the rest of the documentation, we'll focus on the components and workflow of the demo.

Below is what our stats page looked like:

Devcon demo interface