Secure Multi-Party Computation Without Trusted Initializer
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Solution Overview
Problem
Existing technologies face challenges in enabling collaborative computation between multiple platforms without disclosing private or sensitive data, particularly in scenarios where a trusted initializer is not available, leading to inefficiencies and reduced flexibility in data sharing.
Innovation Solution
Implementing a secret sharing scheme that allows parties to perform local computation without a trusted initializer, where the results of local computation are scrambled private data that do not disclose individual party information, enabling secure collaborative computation without revealing original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trusted initializer is used to enable secure multi-party computation, then security and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and removes the trusted initializer component from the secure multi-party computation system. By using secret sharing schemes where parties independently generate and share secrets without requiring a central trusted entity, the system eliminates the complexity and cost associated with setting up and maintaining a trusted initializer while preserving security guarantees through cryptographic protocols
Solution Approach 2:
The patent enables parties to perform self-service secure computation by allowing each party to independently generate their own secret shares and participate in the computation process without relying on an external trusted initializer. The secret sharing scheme allows parties to autonomously establish secure computation capabilities through local secret generation and sharing protocols
2Productivity
If platforms share private data for collaborative computation, then computation accuracy and productivity are improved, but data privacy and security deteriorate
Solution Approach 1:
The patent segments private data into secret shares that are distributed among multiple parties. Each party holds only a portion of the secret information, and no single party can reconstruct the original private data alone. This segmentation allows collaborative computation to proceed on shared data while maintaining privacy, as computations are performed on secret shares rather than raw private data
Solution Approach 2:
The patent introduces secret shares as an intermediary representation of private data. Instead of directly sharing or processing sensitive information, parties exchange and compute on secret shares that mathematically represent the private data without revealing it. This intermediary mechanism enables collaborative computation while preserving data privacy through cryptographic guarantees
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media for secure collaborative computation of a matrix product of a first matrix including private data of a first party and a second matrix including private data of the second party by secret sharing without a trusted initializer. One method includes obtaining a first matrix including private data of the first party; generating a first random matrix; identifying a first sub-matrix and a second sub-matrix of the first random matrix; computing first scrambled private data of the first party based on the first matrix, the first random matrix, the first sub-matrix, and the second sub-matrix; receiving second scrambled private data of the second party; computing a first addend of the matrix product; receiving a second addend of the matrix product; and computing the matrix product by summing the first addend and the second addend.