The Role of Social Values and Ethics in Designing Privacy-Preserving Big Data Systems
Keywords:
Privacy-Preserving Big Data, , Social Values in Technology, Ethical Frameworks, Data ProtectionAbstract
The increase in the adoption of large-scale technology in Pakistan and global has sparked important concerns regarding the handling of user privacy and ethical data. While technical progress allows for excellent data collection and analysis, the inclusion of social values and ethics in the design of big data systems for data protection regulations has not yet been communicated. This article examines the role of social values and ethical frameworks in the design of data protection mechanisms that respect individual autonomy, equity, transparency and trust. We analyze the current state of data protection techniques and the underlined gaps where ethical considerations are often held. Additionally, we propose an integrated design approach to organizing big data privacy solutions with social norms and ethical principles to promote responsible data management. The graphical analysis shows a comparative review of data protection frameworks, including trends in data protection violations, ethical concerns about frequency in the Pakistan digital ecosystem, and ethical standards. This study concludes with the recommendation that political decision makers, system designers, and researchers embed social and ethical values in heavy damage architecture to promote sustainable, user-oriented data protection.
References
Bernhardt, M. (2024). AI Meets TD: Based on a True Story Coming to a Td Function Near You: AI Strengths, Weaknesses, and Limitations. TD Magazine, 78(1), 22-26.
Castets-Renard, C. (2019). Accountability of algorithms in the GDPR and beyond: a European legal framework on automated decision-making. Fordham Intell. Prop. Media & Ent. LJ, 30, 91.
Dwork, C. (2008, April). Differential privacy: A survey of results. In International conference on theory and applications of models of computation (pp. 1-19). Berlin, Heidelberg: Springer Berlin Heidelberg.
Floridi, L. (Ed.). (2016). The Routledge handbook of philosophy of information. London: Routledge.
Mittelstadt, B. D., & Floridi, L. (2016). The ethics of big data: current and foreseeable issues in biomedical contexts. The ethics of biomedical big data, 445-480.
Narayanan, A., & Shmatikov, V. (2008, May). Robust de-anonymization of large sparse datasets. In 2008 IEEE Symposium on Security and Privacy (sp 2008) (pp. 111-125). IEEE.
Nissenbaum, H. (2004). Privacy as contextual integrity. Wash. L. Rev., 79, 119.
Paterson, M., & McDonagh, M. (2018). Data protection in an era of big data: The challenges posed by big personal data. Monash University Law Review, 44(1), 1-31.
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017, May). Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP) (pp. 3-18). IEEE.
Solove, D. J. (2010). Understanding privacy. Harvard university press
