基于区块链的数据版权保护与组合竞拍

doi:10.12066/j.issn.1007-2861.2376

Abstract

Abstract:

A data copyright protection and combinatorial auction system was designed and implemented based on blockchain, digital watermark and sealed bids combinatorial auction. First, a smart contract was applied to store the copyright data and their transaction record via a decentralized technique known as blockchain, which could achieve consensus between each node who did not trust each other. Consequently the system could be rendered more trustworthy. Moreover, a digital watermark was a unique code hidden in data, which could be used to prove the ownership of copyright. The results showed that the watermark module used in the system barely altered original data and was efficient when embedding and extracting watermark. Finally, a combinatorial auction algorithm was designed to select an optimal bid combination automatically to implement the value exchange of data copyright.

Key words: digital copyright, digital watermark, blockchain, smart contract, combinatorial auction

CLC Number:

TP 391

XU Yuqin, QIAN Quan. Blockchain based data copyright protection and combinatorial auction[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 413-426.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.journal.shu.edu.cn/EN/10.12066/j.issn.1007-2861.2376

https://www.journal.shu.edu.cn/EN/Y2022/V28/I3/413

Figures/Tables 14

Fig.1

Table 1

Fig.2

Table 2

Table 3

Fig.3

Table 4

Fig. 4

Table 5

Fig.5

Fig.6

Fig.7

Table 6

Table 7

References 15

[1]	Shi J, Yi D, Kuang J. A blockchain and SIFT based system for image copyright protection[C]// Proceedings of the 2019 2nd International Conference on Blockchain Technology and Applications. 2019: 1-6.
[2]	Xiao L J, Huang W H, Xie Y, et al. A blockchain-based traceable IP copyright protection algorithm[J]. IEEE Access, 2020(8): 49532-49542.
[3]	Wayner P. Digital copyright protection[M]. Salt Lake City: Academic Press Professional Inc, 1997.
[4]	Litman J. Digital copyright[M]. New York: Prometheus Books, 2001.
[5]	Zhao S J, Donal O. Bmcprotector: a blockchain and smart contract based application for music copyright protection[C]// Proceedings of the 2018 International Conference on Blockchain Technology and Application. 2018: 008815.
[6]	Vitalik B. Ethereum whitepaper[EB/OL]. [2022-03-15]. https://ethereum.org/en/whitepaper/.
[7]	Jiang X H. Digital watermarking and its application in image copyright protection[C]// 2010 International Conference on Intelligent Computation Technology and Automation. 2010: 114-117.
[8]	French K R, McCormick R E. Sealed bids, sunk costs, and the process of competition[J]. Journal of Business, 1984, 57(4): 417-441.
[9]	Sandholm T. Algorithm for optimal winner determination in combinatorial auctions[J]. Artificial Intelligence, 2002, 135(1/2): 1-54. doi: 10.1016/S0004-3702(01)00159-X
[10]	Zou W, Lo D, Kochhar P S, et al. Smart contract development: challenges andopportunities[J]. IEEE Transactions on Software Engineering, 2019, 47(10): 2084-2106. doi: 10.1109/TSE.2019.2942301
[11]	Zhang J H. Research on copyright protection technology of digital image based on digital watermarking[D]. Changsha: South-Central University for Nationalities, 2004.
[12]	Kushwaha S C, Das P, Chakraborty M. Multiple watermarking on digital audio based on DWT technique[C]// 2015 International Conference on Communications and Signal Processing (ICCSP). 2015: 15600066.
[13]	Gervais A, Karame G O, Wü S K, et al. On the security and performance of proof of work blockchains[C]// Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016: 3-16.
[14]	Nakamoto S. Bitcoin: a peer-to-peer electronic cash system[EB/OL]. [2022-03-15]. https://bit-coin.org/bitcoin.pdf.
[15]	Castro M, Barbara L. Practical byzantine fault tolerance and proactive recovery[J]. ACM Transactions on Computer Systems, 2002, 20(4): 398-461. doi: 10.1145/571637.571640

Blockchain based data copyright protection and combinatorial auction

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 15

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	CHENG Feng, WAN Weibing, WANG Yuxuan, HU Jimi. Intent-extraction joint model with expertise integration [J]. Journal of Shanghai University（Natural Science Edition）, 2024, 30(4): 655-668.
[2]	CAI Guanhong, LI Guoping, WANG Guozhong, TENG Guowei. Lightweight traffic-light detection algorithm based on improved YOLOv5s [J]. Journal of Shanghai University（Natural Science Edition）, 2024, 30(1): 94-105.
[3]	ZHU He, LU Xiaofeng, XUE Lei. Emotional analysis model of financial text based on the BERT [J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 118-128.
[4]	XIA Ruiling, LI Guoping, WANG Guozhong, TENG Guowei. Personalized learning path recommendation based on improved ant colony algorithm [J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 129-139.
[5]	CHEN Qian, WU Xing. Material data named entity recognition based on matching contextual lexical words and graph convolution [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 372-385.
[6]	YUE Xichao, FENG Yan, LIU Jian, YU Yeyong, XI Kangjie, QIAN Quan. Database for materials genome engineering [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 399-412.
[7]	LIU Fen, FAN Hongqiang, LÜ Tao, LI Qian, QIAN Quan. Kalman filter based method for processing small noisy sample data [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 427-439.
[8]	YAO Yu, HU Tao, FU Jianxun, HU Shunbo. Regression modeling and multi-objective optimization for small sample scattered data [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 451-462.
[9]	ZHANG Peng, ZHANG Rui. Feature selection based on reinforcement learning and its application in material informatics [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 463-475.
[10]	MAI Jiaqi, XU Pengcheng, DING Song, SUN Yangting, LU Wencong. Prediction of pitting potential for stainless steel by support vector regression [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 485-491.
[11]	CHEN Shuizhou, WANG Xiaoshu, OUYANG Qiubao, ZHANG Rui. Data-driven based properties prediction and reverse design of aluminum matrix composites [J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 512-522.
[12]	ZHANG Jin-yi1,2,3, XU De-zheng2, LI Ruo-han1,CHEN Xing-xiu1, XU Qin-le2. 9-axis MEMS-IMU real-time data fusion algorithm for attitude estimation [J]. Journal of Shanghai University（Natural Science Edition）, 2015, 21(5): 547-559.
[13]	SHEN Wen-feng, LI Feng-yong, ZHANG Xin-peng, YANG Qian-xing. Steganographic algorithm with histogram-preserving property [J]. Journal of Shanghai University（Natural Science Edition）, 2015, 21(2): 190-196.
[14]	YANG Qian-xing, LI Feng-yong, ZHANG Xin-peng, SHEN Wen-feng. Adaptive Robust Reversible Data Hiding Based on Modification of DCT Coefficient [J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(5): 605-611.
[15]	ZHANG Qi1, HUANG Chun-chun1, HAN Hong2, LI Chao-lun2, WANG Wen-ping2. CEUS Image Segmentation of Carotid Arteries Using Multi-scale Fuzzy Clustering and DGVF Model [J]. Journal of Shanghai University（Natural Science Edition）, 2014, 20(5): 633-644.