Research on multi-party security collaborative linear regression for computing power networks

PAN Jie; HOU Huifang; CHEN Xi; XUE Zhao; XU Liankun

doi:10.11959/j.issn.1000-0801.2024204

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Research on multi-party security collaborative linear regression for computing power networks

Engineering and Application | 更新时间：2024-09-23

- Research on multi-party security collaborative linear regression for computing power networks
- Telecommunications Science Vol. 40, Issue 8, Pages: 162-171(2024)
- 作者机构：
  
  1.中国移动通信集团设计院有限公司，北京 100080
  2.中国移动通信集团有限公司，北京 100032
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024204
  CLC： TN915.08;TP393.0
- Received：11 June 2024，
  
  Revised：2024-08-15，
  
  Published：20 August 2024
- 稿件说明：
移动端阅览
潘洁,侯慧芳,陈曦等.面向算力网络的多方安全协同线性回归研究[J].电信科学,2024,40(08):162-171.

PAN Jie,HOU Huifang,CHEN Xi,et al.Research on multi-party security collaborative linear regression for computing power networks[J].Telecommunications Science,2024,40(08):162-171.
潘洁,侯慧芳,陈曦等.面向算力网络的多方安全协同线性回归研究[J].电信科学,2024,40(08):162-171. DOI： 10.11959/j.issn.1000-0801.2024204.

PAN Jie,HOU Huifang,CHEN Xi,et al.Research on multi-party security collaborative linear regression for computing power networks[J].Telecommunications Science,2024,40(08):162-171. DOI： 10.11959/j.issn.1000-0801.2024204.

摘要

随着科技的飞速发展，机器学习已经成为推动企业进步的关键因素。然而，对于中小企业而言，数据量和算力的限制常常成为其应用机器学习的障碍。算力网络的兴起则为企业带来了新的机遇，但也伴随着数据安全等新的挑战。提出一种面向算力网络的多用户安全协同计算线性回归方案，该方案允许多个用户使用敏感数据在算力网络中实现安全的联合训练，从而构建线性回归模型。该方案采用低成本盲化手段与同态加密技术对用户敏感数据进行加密处理，从而保护了敏感数据的安全性。

Abstract

With the rapid development of science and technology

machine learning has become a key factor driving the progress of enterprises. However

for small and medium-sized enterprises

the amount of data and computing power often become obstacles to apply machine learning. The rise of computing power networks has brought new opportunities for enterprises

accompanied by new challenges such as data security. A linear regression scheme for multi-user security collaborative computing based on computing power networks was proposed. The scheme allowed multiple users to achieve secure joint training in a computing power network using sensitive data to build a linear regression model. The scheme adopts low-cost blinding method and homomorphic encryption technology to encrypt user sensitive data to protect the security of sensitive data.

关键词

Keywords

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