Secure Multi-Party Computation via Nil Message Compute
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Solution Overview
Problem
Secure Multi Party Computation (SMPC) protocols face significant challenges due to high communication complexity, requiring numerous message exchanges and substantial communication bandwidth, especially when evaluating complex arithmetic functions.
Innovation Solution
The proposed method, known as Nil Message Compute (NMC), allows computing nodes to evaluate arithmetic functions without exchanging messages during the computation phase by using base and exponent blinding factors. These factors are used to obfuscate shares sent from dealer nodes, enabling independent computation by each node without revealing secrets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard SMPC protocols are used to evaluate complex arithmetic functions, then security and privacy are maintained, but communication complexity increases significantly requiring numerous message exchanges and substantial bandwidth
Solution Approach 1:
The patent applies preliminary action by pre-generating additive and multiplicative secret sharing schemes along with random values during an offline preprocessing phase. These pre-computed secrets are stored and reused during the actual computation phase, eliminating the need for complex real-time communication protocols while maintaining security. The preprocessing phase prepares all necessary cryptographic materials in advance, so that the online phase requires minimal interaction between parties.
Solution Approach 2:
The patent segments the computation into distinct phases: preprocessing phase for generating secret shares and random values, and computation phase for evaluating the arithmetic function. It also segments the secret sharing into additive shares and multiplicative shares, each handled independently. This segmentation allows complex secure computation to be broken down into manageable steps with reduced communication overhead in each phase.
2Measurement precision
If numerous message exchanges are performed in SMPC protocols, then accurate computation results are obtained, but computation speed decreases compared to centralized computations
Solution Approach 1:
The patent performs all communication-intensive operations during the offline preprocessing phase, including generation of secret shares and random values. During the online computation phase, parties only need to exchange minimal information (such as evaluating pre-computed polynomials at specific points), which significantly reduces communication latency and improves computation speed while maintaining accuracy through the mathematically sound secret sharing framework.
Solution Approach 2:
The patent uses polynomial evaluation to compute function values at multiple points simultaneously. By evaluating a polynomial at n points, the system obtains n secret shares without requiring n separate communication rounds. This copying approach, where one polynomial evaluation produces multiple useful outputs (shares), reduces the number of message exchanges needed while ensuring accurate reconstruction of the computation result.
Data Source
AI summary
A secure multiparty computation method permits the computation of an arithmetic function ƒ which can be expressed as the addition of A groups of multiplications of a set S of private input secrets {s0, s1, . . . , sS-1}. Dealer nodes holding the secrets are provided with a base blinding factor ρa whose inverse is the sum of a set of pseudorandom numbers each of which is associated with a respective computing node and is not shared with other computing nodes. Each dealer node is further provided with an exponent blinding factor λa,m specific to the secret being contributed, where all of the exponent blinding factors sum to unity. The dealer nodes share with the computing nodes the product of the secrets with the base blinding factor raised to the exponent blinding factor. Each computing nodes can independently and without sharing computations, generate from the product of shares it receives from the dealer nodes a result share. Summing the result shares provides the result of the computation. In this way a sum of products can be computed by the computing nodes without requiring any messages to pass between the computing nodes during the computation.


