Polynomial Encoding for Secure Multi-Party Computation
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Solution Overview
Problem
Existing secure multi-party computation methods face inefficiencies in data privacy protection, particularly when a large number of participant parties are involved, as they require auxiliary data satisfying specific conditions and rely on third-party communication, limiting their applicability.
Innovation Solution
The method encodes private data into coefficients of polynomial functions, splits it into fragments using a secret sharing algorithm, and computes target data without a third party, allowing local computation and improved efficiency, with the ability to recover data using secondary polynomial functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing secure multi-party computation methods are used, then data privacy is protected, but computing efficiency deteriorates and third-party communication is required
Solution Approach 1:
The patent segments private data into multiple fragments using polynomial encoding and secret sharing algorithms. Each participant receives only their fragment, enabling distributed computation without centralized third-party coordination, thus improving efficiency while maintaining privacy
Solution Approach 2:
The patent enables participants to perform computations locally on their own data fragments without requiring third-party assistance. The secret sharing algorithm allows self-contained computation where each party independently processes their fragments to contribute to the final result
2Reliability
If existing secure multi-party computation methods are used, then data privacy is protected, but device complexity increases due to auxiliary data conditions
Solution Approach 1:
The patent extracts and eliminates the requirement for auxiliary data conditions from the computation process. By using direct polynomial encoding and secret sharing, the method removes the complex auxiliary structures needed in traditional approaches, simplifying the system while preserving security
Solution Approach 2:
The patent changes the fundamental parameters of the computation model by using polynomial coefficients directly as shared secrets. This parameter transformation eliminates the need for complex auxiliary data structures and conditions, reducing overall system complexity
3Reliability
If existing secure multi-party computation methods are used, then data privacy is protected, but adaptability deteriorates with larger number of participant parties
Solution Approach 1:
The patent creates a universal computation framework based on polynomial encoding and secret sharing that works consistently regardless of the number of participants. The same core algorithmic structure adapts to any number of parties, enhancing versatility and applicability
Data Source
AI summary
Implementations disclose data processing methods, apparatuses, and computer devices for privacy protection in secure multi-party computation, including encoding private data to a coefficient of a first polynomial function. A plurality of function values of the first polynomial function are obtained as a plurality of fragments obtained after the private data is split, where the fragments of the private data are used for computation by using a secret sharing algorithm to obtain fragments of target data.