差分隐私增强的大米区块链品控模型

doi:10.12133/j.smartag.SA202311027

摘要/Abstract

摘要：

[目的/意义] 针对传统大米品质监管追溯系统中存在的品控数据链机制不够完善、品控信息可追溯程度不足、数据上链效率低及隐私信息泄露等问题，提出一种差分隐私增强的大米区块链品控模型。 [方法] 首先，结合大米全产业链，设计数据传输流程，涵盖种植、收购、加工、仓储和销售等各环节，有效保证品控数据链的连续性；其次，为解决上链数据量大、上链效率低问题，将大米全产业链各环节关键品控数据存储于星际文件系统（InterPlanetary File System, IPFS），然后将存储完成后返回的哈希值上链；最后，为提高品控模型信息可追溯程度，将种植环节关键品控数据中涉及隐私的部分信息通过差分隐私（Differential Privacy）处理后展示给用户，模糊化个体数据，以提高品控信息可信度，同时也保护了农户种植隐私。基于该品控模型，设计了差分隐私增强的大米区块链品控系统，并在相关大米企业实际运行。［结果与讨论］经测试，差分隐私增强的大米区块链品控系统全产业链单环节数据完成存储平均耗时1.125 s，信息追溯查询平均耗时0.691 s。与传统大米品质监管追溯系统相比，单环节数据存储时间缩短6.64%，信息追溯查询时间缩短16.44%。 [结论] 研究提出的模型不仅提高了品控数据连续性和信息可追溯程度，同时保护了农户的隐私，还在一定程度上提升了品控数据存储及信息追溯查询的效率，可为大米品质监管与信息追溯系统的设计和改进提供参考。

关键词: 星际文件系统, 区块链, 品控, 高效上链, 差分隐私增强, 信息追溯

Abstract:

[Objective] Rice plays a crucial role in daily diet. The rice industry involves numerous links, from paddy planting to the consumer's table, and the integrity of the quality control data chain directly affects the credibility of rice quality control and traceability information. The process of rice traceability also faces security issues, such as the leakage of privacy information, which need immediate solutions. Additionally, the previous practice of uploading all information onto the blockchain leads to high storage costs and low system efficiency. To address these problems, this study proposed a differential privacy-enhanced blockchain-based quality control model for rice, providing new ideas and solutions to optimize the traditional quality regulation and traceability system. [Methods] By exploring technologies of blockchain, interplanetary file system (IPFS), and incorporating differential privacy techniques, a blockchain-based quality control model for rice with differential privacy enhancement was constructed. Firstly, the data transmission process was designed to cover the whole industry chain of rice, including cultivation, acquisition, processing, warehousing, and sales. Each module stored the relevant data and a unique number from the previous link, forming a reliable information chain and ensuring the continuity of the data chain for quality control. Secondly, to address the issue of large data volume and low efficiency of blockchain storage, the key quality control data of each link in the rice industry chain was stored in the IPFS. Subsequently, the hash value of the stored data was returned and recorded on the blockchain. Lastly, to enhance the traceability of the quality control model information, the sensitive information in the key quality control data related to the cultivation process was presented to users after undergoing differential privacy processing. Individual data was obfuscated to increase the credibility of the quality control information while also protecting the privacy of farmers' cultivation practices. Based on this model, a differential privacy-enhanced blockchain-based quality control system for rice was designed. [Results and Discussions] The architecture of the differential privacy-enhanced blockchain-based quality control system for rice consisted of the physical layer, transport layer, storage layer, service layer, and application layer. The physical layer included sensor devices and network infrastructure, ensuring data collection from all links of the industry chain. The transport layer handled data transmission and communication, securely uploading collected data to the cloud. The storage layer utilized a combination of traditional databases, IPFS, and blockchain to efficiently store and manage key data on and off the blockchain. The traditional database was used for the management and querying of structured data. IPFS stored the key quality control data in the whole industry chain, while blockchain was employed to store the hash values returned by IPFS. This integrated storage method improved system efficiency, ensured the continuity, reliability, and traceability of quality control data, and provided consumers with reliable information. The service layer was primarily responsible for handling business logic and providing functional services. The implementation of functions in the application layer relied heavily on the design of a series of interfaces within the service layer. Positioned at the top of the system architecture, the application layer was responsible for providing user-centric functionality and interfaces. This encompassed a range of applications such as web applications and mobile applications, aiming to present data and facilitate interactive features to fulfill the requirements of both consumers and businesses. Based on the conducted tests, the average time required for storing data in a single link of the whole industry chain within the system was 1.125 s. The average time consumed for information traceability query was recorded as 0.691 s. Compared to conventional rice quality regulation and traceability systems, the proposed system demonstrated a reduction of 6.64% in the storage time of single-link data and a decrease of 16.44% in the time required to perform information traceability query. [Conclusions] This study proposes a differential privacy-enhanced blockchain-based quality control model for rice. The model ensures the continuity of the quality control data chain by integrating the various links of the whole industry chain of rice. By combining blockchain with IPFS storage, the model addresses the challenges of large data volume and low efficiency of blockchain storage in traditional systems. Furthermore, the model incorporates differential privacy protection to enhance traceability while safeguarding the privacy of individual farmers. This study can provide reference for the design and improvement of rice quality regulation and traceability systems.

Key words: IPFS, blockchain, quality control, efficient on-chain, differential privacy enhancement, information traceability

吴国栋, 胡全兴, 刘旭, 秦辉, 高博文. 差分隐私增强的大米区块链品控模型[J]. 智慧农业(中英文), 2024, 6(4): 149-159.

WU Guodong, HU Quanxing, LIU Xu, QIN Hui, GAO Bowen. Differential Privacy-enhanced Blockchain-Based Quality Control Model for Rice[J]. Smart Agriculture, 2024, 6(4): 149-159.

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参考文献 29

1	TAO Q, CAI Z Y, CUI X H. A technological quality control system for rice supply Chain[J]. Food and energy security, 2023, 12(2): ID e382.
2	张全意, 李太平. 中国大米质量安全研究[J]. 粮食与油脂, 2022, 35(11): 143-146.
	ZHANG Q Y, LI T P. Study on the rice and safety in China[J]. Cereals & oils, 2022, 35(11): 143-146.
3	QIAN L L, ZUO F, ZHANG C D, et al. Geographical origin traceability of rice: A study on the effect of processing precision on index elements[J]. Food science and technology research, 2019, 25(5): 619-624.
4	刘陕南, 刘长征, 张荣华, 等. 基于区块链智能合约的有机大米追溯研究[J]. 中国农机化学报, 2024, 45(1): 217-222, 251.
	LIU S N, LIU C Z, ZHANG R H, et al. Research on organic rice traceability based on blockchain smart contract[J]. Journal of chinese agricultural mechanization, 2024, 45(1): 217-222, 251.
5	王莉, 任健荣, 王涛, 等. 基于区块链的粮食防伪溯源系统的设计与实现[J]. 科学技术与工程, 2023, 23(4): 1625-1634.
	WANG L, REN J R, WANG T, et al. Design and implementation of food security traceability system based on blockchain[J]. Science technology and engineering, 2023, 23(4): 1625-1634.
6	ZHANG Y, WU X Y, GE H Y, et al. A blockchain-based traceability model for grain and oil food supply chain[J]. Foods, 2023, 12(17): ID 3235.
7	卞立平, 吕滢, 罗智彬, 等. 基于区块链技术的食品溯源在元宇宙中的应用构想与设计[J]. 智能化农业装备学报(中英文), 2023, 4(4): 11-19.
	BIAN L P, LYU Y, LUO Z B, et al. Application conception and design of food traceability in the Metaverse based on blockchain technology[J]. Journal of intelligent agricultural mechanization, 2023, 4(4): 11-19.
8	梁昊, 刘思辰, 张一诺, 等. 面向农产品交易流程的多链式区块链应用技术研究[J]. 智慧农业, 2019, 1(4): 72-82.
	LIANG H, LIU S C, ZHANG Y N, et al. Multi-blockchain application technology for agricultural products transaction[J]. Smart agriculture, 2019, 1(4): 72-82.
9	高阳阳, 吕相文, 袁柳等. 基于区块链的农产品安全可信溯源应用研究[J]. 计算机应用与软件, 2020, 37(7): 324-328.
	GAO Y Y, LYU X W, Y L, et al. Application of blockchain-based trusted traceability of agricultural products[J].Computer applications and software, 2020, 37(7): 324-328.
10	王敏学, 李波, 温书凝, 等. 区块链技术赋能食品供应链溯源综述分析[J]. 电子科技大学学报(社科版), 2023, 25(2): 42-54.
	WANG M X, LI B, WEN S N, et al. Reviewing analysis on traceability in food supply chain empowered by blockchain technology[J]. Journal of UESTC (social sciences edition), 2023, 25(2): 42-54.
11	李天明, 严翔, 张增年, 等. 区块链+物联网在农产品溯源中的应用研究[J]. 计算机工程与应用, 2021, 57(23): 50-60.
	LI T M, YAN X, ZHANG Z N, et al. Application research of blockchain+internet of things in agricultural product traceability[J]. Computer engineering and applications, 2021, 57(23): 50-60.
12	查凯金, 王志波, 何月顺, 等. 区块链安全保护研究综述[J]. 计算机与现代化, 2023(6): 110-117.
	ZHA K J, WANG Z B, HE Y S, et al. Survey on blockchain security protection[J]. Computer and modernization, 2023(6): 110-117.
13	SINGH A, GUTUB A, NAYYAR A, et al. Redefining food safety traceability system through blockchain: Findings, challenges and open issues[J]. Multimedia tools and applications, 2023, 82(14): 21243-21277.
14	司冰茹, 肖江, 刘存扬, 等. 区块链网络综述[J]. 软件学报, 2024, 35(2): 773-799.
	SI B R, XIAO J, LIU C Y, et al. Survey on blockchain network[J]. Journal of software, 2024, 35(2): 773-799.
15	GUO H Q, YU X J. A survey on blockchain technology and its security[J]. Blockchain: Research and applications, 2022, 3(2): ID 100067.
16	FAN X, NIU B N, LIU Z L. Scalable blockchain storage systems: Research progress and models[J]. Computing, 2022, 104(6): 1497-1524.
17	WANG X Y, YIN S R. Research on Database Storage Technology based on Consensus Mechanism[C]// Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management. Hangzhou, China: EAI, 2023.
18	PENG X Z, ZHAO Z Y, WANG X Y, et al. A review on blockchain smart contracts in the agri-food industry: Current state, application challenges and future trends[J]. Computers and electronics in agriculture, 2023, 208: ID 107776.
19	ZHAO Y D, LI Q D, YI W L, et al. Agricultural IoT data storage optimization and information security method based on blockchain[J]. Agriculture, 2023, 13(2): ID 274.
20	BALCERZAK A P, NICA E, ROGALSKA E, et al. Blockchain technology and smart contracts in decentralized governance systems[J]. Administrative sciences, 2022, 12(3): ID 96.
21	ATHANERE S, THAKUR R. Blockchain based hierarchical semi-decentralized approach using IPFS for secure and efficient data sharing[J]. Journal of king Saud university-computer and information sciences, 2022, 34(4): 1523-1534.
22	陈泓达, 冯云霞, 牛云鹤. 基于IPFS区块链技术的工业互联网数据可信存储系统[J]. 计算机科学与应用, 2022, 12: ID 1292.
	CHEN H D, FENG Y X, NIU Y H. Industrial internet data trusted storage system based on IPFS blockchain technology[J].Computer science and application, 2022, 12: ID 1292.
23	王腾, 霍峥, 黄亚鑫, 等. 联邦学习中的隐私保护技术研究综述[J]. 计算机应用, 2023, 43(2): 437-449.
	WANG T, HUO Z, HUANG Y X, et al. Review on privacy-preserving technologies in federated learning[J]. Journal of computer applications, 2023, 43(2): 437-449.
24	WU X T, QI L Y, GAO J Q, et al. An ensemble of random decision trees with local differential privacy in edge computing[J]. Neurocomputing, 2022, 485: 181-195.
25	ADNAN M, KALRA S, CRESSWELL J C, et al. Federated learning and differential privacy for medical image analysis[J]. Scientific reports, 2022, 12: ID 1953.
26	SARKAR A, SHARMA A, GILL A, et al. A differential privacy-based system for efficiently protecting data privacy[C]// 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS). Piscataway, New Jersey, USA: IEEE, 2023: 1399-1404.
27	VASA J, THAKKAR A. Deep learning: Differential privacy preservation in the era of big data[J]. Journal of computer information systems, 2023, 63(3): 608-631.
28	LI J T, HAN D Z, WU Z D, et al. A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control[J]. Future generation computer systems, 2023, 142(C): 195-211.
29	WU Q T, LI M W, ZHU J L, et al. DP-RBAdaBound: A differentially private randomized block-coordinate adaptive gradient algorithm for training deep neural networks[J]. Expert systems with applications, 2023, 211: ID 118574.

全产业链环节	公开可追溯数据	差分隐私处理可追溯数据	不可追溯数据
种植	种植编号、稻谷品种、种植日期、种植地区、土壤类型、施肥量、降水量、灌溉量、光照强度	农药制剂平均用量、土壤碱解氮含量、种植密度	农户信息、种植量、种植成本、利润、灾害损失
收购	收购编号、农户编号、稻谷等级、稻谷名称、收购日期	—	收购量、收购成本、利润
加工	加工编号、加工日期、加工方式、大米等级、打包日期、包装编号、打包方式	—	加工量、出米量、打包量、加工成本、利润
仓储	仓储编号、仓库名称、仓库温度、仓库湿度、入库时间、出库时间	—	仓储数量、仓储成本、利润
销售	订单编号、销售日期、销售用户、产品编号、产品名称、产品批次、产品重量、产品价格、发货编号、发货日期	—	销售成本、销售利润

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