Citation: | Dai Weiqi, Li Ming, Zhao Kexuan, Jiang Wenchao, Zhou Weilin, Zou Deqing, Jin Hai. Blockchain Marketing Label Trading System for E-Commerce Alliance[J]. Journal of Computer Research and Development, 2025, 62(1): 269-280. DOI: 10.7544/issn1000-1239.202330217 |
In the era of big data e-commerce, data trading can enable collaborative sharing and value utilization of isolated data resources. As the main form of data trading in e-commerce business, marketing tags have enormous potential value. However, the traditional data trading market faces three main problems: 1) The opaque information of the centralized platform leads to trust crisis and malicious bidding ranking; 2) Lack of reasonable incentive mechanism to break the data island leads to data non-circulation and sharing difficulties; 3) Data security threats lead to privacy disclosure and data reselling and theft. In order to solve these problems, a blockchain marketing label trading mechanism for e-commerce alliance is designed, and the upper consensus incentive mechanism is designed based on decentralization, and all data transactions and computing businesses of the system are completed in combination with the trusted execution environment, thus realizing a safe and complete data transaction ecosystem. The authenticity verification mechanism is designed to ensure the effectiveness of marketing labels, the consensus incentive mechanism is designed to enable users to actively share data, and the smart contract is used to effectively constrain the role behavior according to the system design specification; Then key transmission and data security storage are realized through SGX remote authentication, and smart contract security call is realized to ensure user privacy and data security; Finally, the reliable delivery of data transaction results is realized through the trusted computer system and system design idea. In order to verify the security and practicability of the system,
[1] |
祝烈煌,高峰,沈蒙,等. 区块链隐私保护研究综述[J]. 计算机研究与发展,2017,54(10):2170−2186 doi: 10.7544/issn1000-1239.2017.20170471
Zhu Liehuang, Gao Feng, Shen Meng, et al. Survey on privacy preserving techniques for blockchain technology[J]. Journal of Computer Research and Development, 2017, 54(10): 2170−2186 (in Chinese) doi: 10.7544/issn1000-1239.2017.20170471
|
[2] |
Sani A S, Bertino E, Yuan Dong, et al. SPrivAD: A secure and privacy-preserving mutually dependent authentication and data access scheme for smart communities[J]. Computers & Security, 2022, 115: 102610
|
[3] |
杨琪,龚南宁. 我国大数据交易的主要问题及建议[J]. 大数据,2015,1(2):38−48 doi: 10.11959/j.issn.2096-0271.2015017
Yang Qi, Gong Nanning. Main problems and suggestions on China’s big data trading[J]. Big Data, 2015, 1(2): 38−48 (in Chinese) doi: 10.11959/j.issn.2096-0271.2015017
|
[4] |
Zheng Xiao. Data trading with differential privacy in data market[C]//Proc of the 6th Int Conf on Computing and Data Engineering. New York: ACM, 2020: 112−115
|
[5] |
Lu Siqi, Zheng Jianhua, Cao Zhenfu, et al. A survey on cryptographic techniques for protecting big data security: Present and forthcoming [J]. Science China Information Sciences. 2022, 65(10): 201301
|
[6] |
周炜,王超,徐剑,等. 基于区块链的隐私保护去中心化联邦学习模型[J]. 计算机研究与发展,2022,59(11):2423−2436 doi: 10.7544/issn1000-1239.20220470
Zhou Wei, Wang Chao, Xu Jian, et al. Privacy-preserving and decentralized federated learning model based on the blockchain[J]. Journal of Computer Research and Development, 2022, 59(11): 2423−2436 (in Chinese) doi: 10.7544/issn1000-1239.20220470
|
[7] |
Li Hui, Pei Lishuang, Liao Dan, et al. BDDT: Use blockchain to facilitate IoT data transactions[J]. Cluster Computing, 2021, 24: 459−473 doi: 10.1007/s10586-020-03119-w
|
[8] |
石丹. 大数据时代数据权属及其保护路径研究[J]. 西安交通大学学报:社会科学版,2018,38(3):78−85
Shi Dan. Research on data ownership and protection path in the age of big data[J]. Journal of Xi’an Jiaotong University: Social Science Edition, 2018, 38(3): 78−85 (in Chinese)
|
[9] |
Maesa D D F, Mori P. Blockchain 3.0 applications survey[J]. Journal of Parallel and Distributed Computing, 2020, 138(12): 99−114
|
[10] |
Wendl M, Doan M H, Sassen R. The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review[J]. Journal of Environmental Management, 2023, 326: 116530 doi: 10.1016/j.jenvman.2022.116530
|
[11] |
应臣浩,夏福源,李颉,等. 区块链群智感知中基于隐私数据真值估计的激励机制[J]. 计算机研究与发展,2022,59(10):2212−2232 doi: 10.7544/issn1000-1239.20220493
Ying Chenhao, Xia Fuyuan, Li Jie, et al. Incentive mechanism based on truth estimation of private data for blockchain-based mobile crowdsensing[J]. Journal of Computer Research and Development, 2022, 59(10): 2212−2232 (in Chinese) doi: 10.7544/issn1000-1239.20220493
|
[12] |
Chernyavskiy A, Ilvovsky D. Extract and aggregate: A novel domain-independent approach to factual data verification[C]//Proc of the 2nd Workshop on Fact Extraction and VERification (FEVER). Stroudsburg, PA: ACL, 2019: 69−78
|
[13] |
Xiao Yang, Zhang Ning, Li Jin, et al. PrivacyGuard: Enforcing private data usage control with blockchain and attested off-chain contract execution[C]//Proc of the 25th European Symp on Research in Computer Security. Berlin: Springer, 2020: 610−629
|
[14] |
Yao Xin, Yan Xiaoping, Zhao Ming. Authenticity verification for dynamic social data outsourcing[J]. IEEE Systems Journal, 2021, 16(2): 2325−2335
|
[15] |
Liu Jinhui, Yu Yong, Bi Hongliang, et al. Post quantum secure fair data trading with deterability based on machine learning[J]. Science China Information Sciences, 2022, 65(7): 170308 doi: 10.1007/s11432-021-3441-y
|
[16] |
Li Yanan, Feng Xiaotao, Xie Jan, et al. A decentralized and secure blockchain platform for open fair data trading[J]. Concurrency and Computation: Practice and Experience, 2020, 32(7): e5578 doi: 10.1002/cpe.5578
|
[17] |
赵志伟. 基于区块链的个人数据交易隐私保护研究[D]. 成都:电子科技大学,2020
Zhao Zhiwei. Research on the privacy protection of personal data trading based on blockchain [D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese)
|
[18] |
夏昌琳. 基于区块链技术的物联网数据交易系统[D]. 南京:南京邮电大学,2020
Xia Changlin. Internet of things data trading system based on blockchain technology [D]. Nanjing: Nanjing University of Posts and Telecommunications, 2020 (in Chinese)
|
[19] |
Knauth T, Steiner M, Chakrabarti S, et al. Integrating remote attestation with transport layer security [J]. arXiv preprint, arXiv: 1801.05863, 2018
|
[20] |
Sardar M U, Fetzer C. Towards formalization of enhanced privacy ID (EPID)-based remote attestation in Intel SGX[C]//Proc of the 23rd Euromicro Conf on Digital System Design (DSD). Piscataway, NJ: IEEE, 2020: 604−607
|
[21] |
Karande V, Bauman E, Lin Zhiqiang, et al. SGX-Log: Securing system logs with SGX[C]//Proc of the 2017 ACM on Asia Conf on Computer and Communications Security. New York: ACM, 2017: 19−30
|
[22] |
Kuang Boyu, Fu Anmin, Susilo W, et al. A survey of remote attestation in Internet of things: Attacks, countermeasures, and prospects[J]. Computers & Security, 2022, 112: 102498
|
[23] |
Lei Hong, Bao Zijian, Wang Qinghao, et al. SDABS: A secure cloud data auditing scheme based on blockchain and SGX[C]//Proc of the 2nd Blockchain and Trustworthy Systems. Berlin: Springer, 2020: 269−281
|
[24] |
Dai Weiqi, Dai Chunkai, Choo K K R, et al. SDTE: A secure blockchain-based data trading ecosystem[J]. IEEE Transactions on Information Forensics and Security, 2019, 15: 725−737
|
[1] | Zhao Anning, Xu Nuo, Liu Kang, Luo Li, Pan Bingzheng, Bo Ziyi, Tan Chenghao. The Synthesis of Multiple Stateful Logic Gates for In-memory Computing with Low Wear[J]. Journal of Computer Research and Development, 2025, 62(3): 620-632. DOI: 10.7544/issn1000-1239.202440627 |
[2] | Zhang Zhang, Shi Gang, Wang Qifan, Ma Yongbo, Liu Gang, Qian Libo. Survey of In-Memory Computing Technology Based on SRAM and Non-Volatile Memory[J]. Journal of Computer Research and Development, 2024, 61(12): 2937-2951. DOI: 10.7544/issn1000-1239.202330364 |
[3] | Wei Zheng, Zhang Xingjun, Zhuo Zhimin, Ji Zeyu, Li Yonghao. PPO-Based Automated Quantization for ReRAM-Based Hardware Accelerator[J]. Journal of Computer Research and Development, 2022, 59(3): 518-532. DOI: 10.7544/issn1000-1239.20210551 |
[4] | Ou Yan, Feng Yujing, Li Wenming, Ye Xiaochun, Wang Da, Fan Dongrui. Optimum Research on Inner-Inst Memory Access Conflict for Dataflow Architecture[J]. Journal of Computer Research and Development, 2019, 56(12): 2720-2732. DOI: 10.7544/issn1000-1239.2019.20190115 |
[5] | Mao Haiyu, Shu Jiwu. 3D Memristor Array Based Neural Network Processing in Memory Architecture[J]. Journal of Computer Research and Development, 2019, 56(6): 1149-1160. DOI: 10.7544/issn1000-1239.2019.20190099 |
[6] | Liu Bicheng, Gu Haifeng, Chen Mingsong, Gu Shouzhen, Chen Wenjie. An Efficient Processing In Memory Framework Based on Skyrmion Material[J]. Journal of Computer Research and Development, 2019, 56(4): 798-809. DOI: 10.7544/issn1000-1239.2019.20180157 |
[7] | Pan Fengfeng, Xiong Jin. NV-Shuffle: Shuffle Based on Non-Volatile Memory[J]. Journal of Computer Research and Development, 2018, 55(2): 229-245. DOI: 10.7544/issn1000-1239.2018.20170742 |
[8] | Bian Chen, Yu Jiong, Xiu Weirong, Qian Yurong, Ying Changtian, Liao Bin. Partial Data Shuffled First Strategy for In-Memory Computing Framework[J]. Journal of Computer Research and Development, 2017, 54(4): 787-803. DOI: 10.7544/issn1000-1239.2017.20160049 |
[9] | Wang Jinbao, Gao Hong, Li Jianzhong, Yang Donghua. Processing String Similarity Search in External Memory Efficiently[J]. Journal of Computer Research and Development, 2015, 52(3): 738-748. DOI: 10.7544/issn1000-1239.2015.20130683 |
[10] | Shen Huanghui, Wang Zhensong, Zheng Weimin. An Efficient Memory Access Strategy for Transposition and Block Operation in Image Processing[J]. Journal of Computer Research and Development, 2013, 50(1): 188-196. |
1. |
梁志闯,赵旭阳,方博越,赵运磊. 素阶数域上的高效紧凑NTRU密钥封装方案. 软件学报. 2025(02): 747-775 .
![]() |