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A Review of Ethical Issues in the Field of Cybersecurity
- 1 Beihang University, Beijing, China, 100080
* Author to whom correspondence should be addressed.
Abstract
The ethical dilemma of cybersecurity in the era of big data presents a multi-dimensional outbreak trend. As the speed of technological change far exceeds the update of laws and regulations, the crisis of technological trust and social ethical conflicts are intertwined, forcing the reconstruction of the global governance system. This study comprehensively deconstructs the ethical disputes in the current field of cybersecurity and summarizes five core contradictions, including the zero-sum game between monitoring rights and privacy rights, the systematic spread of algorithmic discrimination, the new hegemony of data colonialism, the gap between rights and responsibilities of vulnerability disclosure, and the failure of the AI application attribution mechanism. Based on the comprehensive analysis and systematic integration of multi-dimensional fragmented cases, this study proposes a hierarchical and progressive governance paradigm: the technical governance layer pragmatically improves the existing system, the institutional coordination layer links multiple mechanisms, and the cultural identity layer improves the digital citizen literacy. By balancing the dual logic of technological innovation and value constraints, this framework provides an operational governance path for cybersecurity regulatory departments to optimize ethical risk assessment tools, Internet companies to establish algorithm audit committees, and technology research and development institutions to improve ethical embedded design. It has important theoretical reference value for building a humanistic-oriented digital civilization order.
Keywords
Cybersecurity, ethics, artificial intelligence
[1]. Yanyan Qiu. Cybersecurity and Ethics Construction [J]. Intelligence Magazine, 2000, 19(1):17-18, 21. DOI:10.3969/j.issn.1002-1965.2000.01.006.
[2]. Yongjiang Xie, Lin Zhu, Jie Shang. The European and American Privacy Shield Agreement and its Implications for my country [J]. Journal of Beijing University of Posts and Telecommunications (Social Sciences Edition), 2016, 18(6):39-44. DOI:10.3969/j.issn.1008-7729.2016.06.007.
[3]. International Association of Privacy Professionals. (2022). Data governance in the age of hyperconnectivity: 2022 whitepaper. IAPP Resource Center. https://iapp.org/resource-center/white-papers.
[4]. Federal Trade Commission. (2023) FTC Sends Refunds to Ring Customers Stemming from 2023 Settlement over Charges the Company Failed to Block Employees and Hackers from Accessing Consumer Videos.
[5]. Buolamwini, J., Gebru, T. (2018) Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research Conference on Fairness, Accountability, and Transparency, 81:1–15, 2018.
[6]. Huaijun Wang, Xuhua Ru. Discrimination in artificial intelligence algorithms and its governance [J]. Research in Philosophy of Science and Technology, 2020, 37(2):101-106.
[7]. Couldry N., Mejias U. A. The costs of connection: how data is colonizing human life and appropriating it for capitalism [M]. California: Stanford University Press, 2019.
[8]. Shuai Li, Zhenbang Li. Research on data colonialism from the perspective of information politics [J]. Journal of Changsha University of Science and Technology (Social Science Edition), 2024, 39(2):33-41. DOI:10.16573/j.cnki.1672-934x.2024.02.005.
[9]. Ye Lou, Wenying Gao. Suggestions on the legalization of "white hat" hacker vulnerability detection behavior [J]. Journal of Hunan Institute of Engineering (Social Science Edition), 2019, 29(1):74-80. DOI:10.3969/j.issn.1671-1181.2019.01.016.
[10]. Yanhong Liu, Shanyao Gong. Research on the criminal liability of network service providers for AI decisions [J]. Journal of Guangxi University (Philosophy and Social Sciences Edition), 2022, 44(3):164-173. DOI:10.13624/j.cnki.jgupss.2022.03.005.
[11]. Shufen Zhang, Hongyang Zhang, Zhiqiang Ren, et al. A review of fairness in federated learning [J]. Journal of Computer Applications, 2025, 45(1):1-14. DOI:10.11772/j.issn.1001-9081.2023121881.
[12]. Zhiqiang Gao, Yutao Wang. Research progress of differential privacy technology [J]. Journal of Communications, 2017, 38(z1):151-155. DOI:10.11959/j.issn.1000-436x.2017241.
[13]. Wenke Zhang, Yong Yang, Yu Yang. Research on secure multi-party computation [J]. Information Security and Communication Confidentiality, 2014(1):97-99. DOI:10.3969/j.issn.1009-8054.2014.01.027.
[14]. Huihui Jia, Chao Wang, Xiangmin Ji. Research on key technologies of data desensitization [J]. Microcomputer Applications, 2024, 40(6):164-167. DOI:10.3969/j.issn.1007-757X.2024.06.041.
[15]. Reimers, Christian, et al. Conditional adversarial debiasing: Towards learning unbiased classifiers from biased data. DAGM German Conference on Pattern Recognition. Cham: Springer International Publishing, 2021.
[16]. Geng Zhao. Research on the application of confidence-building measures in the formulation of international cybersecurity rules [J]. Intelligence Magazine, 2022, 41(11):89-96, 47. DOI:10.3969/j.issn.1002-1965.2022.11.014.
[17]. Qiang Li, Zeng Wei. International collaboration in cybersecurity governance [J]. China Science and Technology Forum, 2016(11):26-31. DOI:10.3969/j.issn.1002-6711.2016.11.005.
[18]. Lei Wang. Bottom-up norm formulation and the generation of international cybersecurity norms [J]. International Security Studies, 2022, 40(5):130-156. DOI:10.14093/j.cnki.cn10-1132/d.2022.05.006.
Cite this article
Cui,Z. (2025). A Review of Ethical Issues in the Field of Cybersecurity. Applied and Computational Engineering,150,125-132.
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