Sampling from binomial distribution in secure multiparty computation

The offline-online model in secure multiparty computation generates random noise using Hamming weight-based binomial distribution, addressing privacy leaks in MPC by reducing complexity and costs, ensuring secure and efficient differential privacy.

WO2025160710A1 Publication Date: 2025-08-07BEIJING ZITIAO NETWORK TECH CO LTD +1
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Patent Information

Application Number
PCT/CN2024/074509
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Filing Date
2024-01-29
Publication Date
2025-08-07

AI Technical Summary

Technical Problem

Existing secure multiparty computation (MPC) protocols face challenges in maintaining differential privacy, as function results can leak information about private inputs, necessitating the introduction of random noise, but current methods are computationally complex and inefficient.

Method used

An offline-online model is employed where offline parties generate an n-bit random binary number and function table segments, which online parties use to calculate a share of random noise based on Hamming weight, simulating a binomial distribution, reducing computational complexity and communication costs.

Benefits of technology

This approach efficiently generates random noise for MPC, providing differential privacy with reduced computational and communication overhead, allowing secure computation without revealing individualized information.

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Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a random noise value for use in providing differential privacy in multiparty computation. One of the method includes receiving, by a first party participating in a multiparty secure computation ( "MPC" ) instance, a share of a first n-bit random binary number and a plurality of function table segments; generating a share of a second n-bit random binary number; calculating a first value based on a comparison between the share of the first and second n-bit random binary numbers; reconstructs an overall value of a comparison between the first and second n-bit random binary numbers; and determining a share of the random noise value based on the overall value and the plurality of function table segments.
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