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Digital Rights Management (DRM) technologies and legal research: Applications and regulations of encryption, digital watermarking, and copyright protection systems
- 1 Tsinghua University, Beijing, China
- 2 University of California, Berkeley, United States
* Author to whom correspondence should be addressed.
Abstract
This paper explores the intersection of technology and law in Digital Rights Management (DRM) systems. By examining the application and regulation of encryption technologies, digital watermarking, and comprehensive copyright protection systems, this study provides a detailed understanding of how these technologies prevent unauthorized copying, distribution, and use of digital content. The paper introduces mathematical models to quantify the effectiveness of encryption and watermarking, offering insights into their practical applications and implications for intellectual property law. The comprehensive DRM effectiveness model combines these individual models, accounting for user inconvenience, to provide a holistic view of DRM system performance. Key case studies illustrate the implementation of DRM technologies in various industries, highlighting best practices and regulatory compliance. The study concludes with recommendations for future research and policy development to enhance the effectiveness and legal robustness of DRM technologies. This work contributes to the academic and practical understanding of DRM, offering a framework for optimizing DRM strategies in a dynamic digital landscape.
Keywords
Digital Rights Management, DRM, Encryption, Digital Watermarking, Copyright Protection.
[1]. Calzada, Igor, Marc Pérez-Batlle, and Joan Batlle-Montserrat. "People-centered smart cities: An exploratory action research on the cities’ coalition for digital rights." Journal of Urban Affairs 45.9 (2023): 1537-1562.
[2]. Frolova, E. E., and E. V. Kupchina. "Digital Tools for the Protection of Intellectual Property Rights: A Case Study of Blockchain and Artificial Intelligence." Perm U. Herald Jurid. Sci. 61 (2023): 479.
[3]. Bühler, Michael Max, et al. "Unlocking the power of digital commons: Data cooperatives as a pathway for data sovereign, innovative and equitable digital communities." Digital 3.3 (2023): 146-171.
[4]. Eke, Damian, and Bernd Stahl. "Ethics in the Governance of Data and Digital Technology: An Analysis of European Data Regulations and Policies." Digital Society 3.1 (2024): 11.
[5]. Ngwenyama, Ojelanki, Helle Zinner Henriksen, and Daniel Hardt. "Public management challenges in the digital risk society: A critical analysis of the public debate on implementation of the Danish NemID." European Journal of Information Systems 32.2 (2023): 108-126.]
[6]. Alexan, Wassim, et al. "Color image encryption through chaos and kaa map." IEEE Access 11 (2023): 11541-11554.
[7]. Hohenberger, Susan, et al. "Registered attribute-based encryption." Annual International Conference on the Theory and Applications of Cryptographic Techniques. Cham: Springer Nature Switzerland, 2023.
[8]. Alawida, Moatsum. "A novel chaos-based permutation for image encryption." Journal of King Saud University-Computer and Information Sciences 35.6 (2023): 101595.
[9]. Lancaster, Thomas. "Artificial intelligence, text generation tools and ChatGPT–does digital watermarking offer a solution?." International Journal for Educational Integrity 19.1 (2023): 10.
[10]. Nematollahi, Mohammad Ali. "A machine learning approach for digital watermarking." Australian Journal of Multi-Disciplinary Engineering 19.1 (2023): 53-63.
Cite this article
Han,L.;Liu,M. (2024). Digital Rights Management (DRM) technologies and legal research: Applications and regulations of encryption, digital watermarking, and copyright protection systems. Applied and Computational Engineering,82,106-111.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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