Abstract
Data watermarking technology is an effective means to protect the copyright of big data. In order to embed robust and highly available data watermarks, firstly, based on the game theory, a Nash equilibrium model between watermark robustness and data quality is established to solve the optimal number of data group. Then, the mapping relationship between data group and watermark bit is established by using secure hash algorithm.Finally, under the constraint of data usability, the improved particle swarm optimization algorithm is used to solve the optimal solution of data change for each data group, and then the data is changed accordingly to complete the embedding of watermark bit. In order to verify the copyright ownership of big data, the corresponding watermark extraction method is also given in this paper. Watermark extraction is the inverse process of watermark embedding. First, it traverses all the groups and extracts the bits that might be embedded in each group. Then, the actual embedded watermark bit is finally determined by most election strategies. The experimental results show that the proposed method can not only detect watermarks under different attack conditions, ensure the robustness of big data watermarks, but also achieve better data quality, and the comprehensive effect of data watermarks is better than the existing methods.
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Acknowledgements
This research is supported by Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (No.ICDDXN004), Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University and Key Lab of Information Network Security of Ministry of Public Security (No.C18601).
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This article is part of the Topical Collection: Special Issue on Privacy-Preserving Computing
Guest Editors: Kaiping Xue, Zhe Liu, Haojin Zhu, Miao Pan and David S.L. Wei
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Xu, Y., Shi, B. Copyright protection method of big data based on nash equilibrium and constraint optimization. Peer-to-Peer Netw. Appl. 14, 1520–1530 (2021). https://doi.org/10.1007/s12083-021-01096-4
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DOI: https://doi.org/10.1007/s12083-021-01096-4