MPC-Blockchain Computing for Verifiable Confidential Data Sharing
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Solution Overview
Problem
Existing data management systems face challenges in deriving unique insights from segregated data silos due to regulatory restrictions, leading to inefficiencies in data storage and computation effectiveness, and conventional methods involving trusted third parties are ineffective due to difficulty in agreement among independent entities.
Innovation Solution
A system combining multi-party computation (MPC) and blockchain technology, utilizing smart contracts to enforce governance and perform privacy-preserving computations off-chain, ensuring data privacy and integrity through hashing and resharing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is segregated into independent data silos to comply with regulations, then data privacy and security are improved, but data storage efficiency and computation effectiveness deteriorate
Solution Approach 1:
The patent divides private data into multiple secret shares using secret sharing schemes, distributing them across different controller nodes. This segmentation allows computations to be performed on distributed shares without reconstructing the original data, maintaining privacy while enabling effective computation across what were previously segregated data silos.
Solution Approach 2:
The patent introduces trusted execution environments (TEEs) and secure multi-party computation (MPC) protocols as intermediaries that enable computations on encrypted or shared data without exposing the underlying private information. These intermediaries act as mediators between the need for data privacy and the need for computation effectiveness.
2Productivity
If data is shared across jurisdictions to improve algorithm effectiveness, then computation effectiveness is improved, but data security and regulatory compliance deteriorate
Solution Approach 1:
By segmenting data into secret shares distributed across multiple controller nodes in different jurisdictions, the system enables algorithms to access combined data for improved effectiveness while maintaining security through the mathematical properties of secret sharing that prevent reconstruction from individual shares.
Solution Approach 2:
The patent creates a universal computing framework that can operate across multiple jurisdictions with different regulatory requirements. The secure computation platform provides multi-functional capability to handle diverse data types and computation tasks while maintaining a single security model based on cryptographic protocols and TEEs.
3Productivity
If an independent third party is used to perform computations on combined data, then computation effectiveness is improved, but system complexity and agreement difficulty increase
Solution Approach 1:
The patent eliminates the need for a single independent third party by segmenting the computation function across multiple controller nodes that each hold secret shares. This distributed approach reduces system complexity by removing the coordination burden of selecting and managing a single trusted third party while maintaining computation effectiveness through collaborative processing of shared data.
Solution Approach 2:
The system enables data owners to perform computations on their own distributed data through the secure multi-party computation framework without requiring external intermediaries. The cryptographic protocols and TEEs provide self-service capabilities that allow organizations to maintain control over their data while achieving effective computation.
4Reliability
If data is stored in individual silos to maintain security, then data privacy is improved, but storage efficiency deteriorates
Solution Approach 1:
The patent segments data into compact secret shares that can be distributed across multiple nodes. This segmentation enables more efficient storage utilization because the same data can be securely accessed by multiple parties without requiring redundant full copies in each silo, reducing overall storage requirements while maintaining privacy.
Data Source
AI summary
The techniques described herein relate to systems and methods for executing a verifiable confidential computation using both an MPC network and a blockchain network. An exemplary method includes: assigning using at least one smart contract in the blockchain network: a first owner node that stores private data, a plurality of controller nodes that each store a respective data share of the private data, a first analyst node that indicates a respective computation of the secure MPC to execute on each corresponding data share stored on the plurality of controller nodes, and a plurality of processor nodes that execute each respective computation of the secure MPC on each corresponding data share and output a result; executing the secure MPC on the private data of the first owner node using the plurality of controller nodes, the first analyst node, and the plurality of processor nodes; recording the result on a blockchain.