食品科学 ›› 2024, Vol. 45 ›› Issue (15): 1-12.doi: 10.7506/spkx1002-6630-20231009-046

• 基础研究 •    下一篇

基于区块链和联邦学习的食品全程全息风险信息可信共享模式

张新, 谭学泽, 王小艺, 赵峙尧, 于家斌, 许继平   

  1. (1.北京工商大学计算机与人工智能学院, 北京 100048;2.北京工商大学 北京市食品安全大数据技术重点实验室, 北京 100048;3.中国音乐学院, 北京 100101)
  • 出版日期:2024-08-15 发布日期:2024-08-04
  • 基金资助:
    “十四五”国家重点研发计划重点专项(2022YFF1101103);北京市自然科学基金项目(4222042); 北京市属高等学校优秀青年人才培育计划项目(BPHR202203043)

A Trusted Sharing Model for Risk Information of Food Full-Process and All-Information Based on Blockchain and Federated Learning

ZHANG Xin, TAN Xueze, WANG Xiaoyi, ZHAO Zhiyao, YU Jiabin, XU Jiping   

  1. (1. School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; 2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; 3. China Conservatory of Music, Beijing 100101, China)
  • Online:2024-08-15 Published:2024-08-04

摘要: 针对传统共享模式下食品全程全息风险信息敏感易泄露、中心化服务器和恶意节点的自利性与完全自治化易产生恶意行为、跨环节交互困难、单一目的导致的数据利用率低等问题, 本研究提出一种基于区块链和联邦学习的食品全程全息风险信息可信共享模式。首先, 在分析食品企业和监管机构信息共享需求的基础上, 将联邦学习融入分层区块链架构中, 并结合中继思想, 构建食品全程全息风险信息可信共享模型;其次, 以风险信息特征和类别为不同侧重点, 将模型联邦学习过程划分为横向和纵向联邦学习过程, 并用两种联邦学习聚合算法实现对数据的聚合;再次, 针对企业内、企业间、企业监管间共享风险信息数据的不同需求, 利用同态加密算法和零知识证明思想, 实现对不同敏感度风险信息的分级加密与定向可信共享;最后, 以食品安全风险评估为仿真背景, 基于食品风险信息公开数据集和开源平台对本模型进行仿真验证。结果表明, 所构建的基于区块链和联邦学习的食品全程全息风险信息可信共享模型在满足企业与监管机构各方对风险信息数据不同共享需求的同时, 能够实现对风险信息数据的充分利用和安全可信、高效精准的共享, 从而增强食品企业与监管机构共享数据的决心, 促进食品行业数据可信共享和食品安全数字化的发展。

关键词: 食品全程全息;区块链;联邦学习;同态加密;可信共享

Abstract: In the traditional sharing mode, the risk information of food full-process and all-information is sensitive and easy to leak, the self-interest and complete autonomy of centralized servers and malicious nodes easily give rise to malicious behaviors, cross-link interactions are difficult to deal with, and the single purposes causes low utilization of data. In view of these problems, this paper proposes a trusted sharing model for risk information of food full-process and all-information based on blockchain and federated learning. First, based on analysis of the information sharing needs of food companies and regulatory agencies, federal learning was integrated into a hierarchical blockchain architecture, and the relay concept was used to construct the trusted sharing model. Next, the federated learning process was divided into horizontal and vertical federated learning processes based on the characteristics and categories of risk information, and two federated learning aggregation algorithms were used to achieve the aggregation of data. Then, in response to the different needs of sharing risk information within and between enterprises, and between enterprises and regulatory agencies, homomorphic encryption algorithms and zero-knowledge proof were utilized for hierarchical encryption and targeted trusted sharing of risk information with different sensitivities. Finally, this model was validated by applying it to food safety risk assessment based on the food risk information disclosure dataset and open-source platforms. The results showed that the trusted sharing model could meet the different risk information sharing needs of enterprises and regulatory agencies, and achieve the full utilization as well as safe, credible, efficient and accurate sharing of risk information, thereby strengthening the determination of food enterprises and regulatory agencies to share data, and promoting the development of trusted data sharing and food safety digitalization in the food industry.

Key words: food full-process and all-information; blockchain; federated learning; homomorphic encryption; trusted sharing

中图分类号: