DATA PRIVACY IN E-COMMERCE BUSINESS: CHALLENGES AND LEGAL SOLUTIONS

Authors

  • Gunawan Widjaja Faculty of Law Universitas 17 Agustus 1946 Jakarta
  • Hotmaria Hertawaty Sijabat Doctoral Student Faculty of Law Universitas 17 Agustus 1945 Jakarta

Keywords:

Data Privacy, E-Commerce Business, Challenges, Legal Solutions

Abstract

This study discusses the challenges and legal solutions related to data privacy in e-commerce businesses, which is becoming increasingly important as the volume of online transactions increases. Consumer data privacy faces significant risks due to cybersecurity threats and the complexity of cross-jurisdictional regulations. To overcome these challenges, a series of legal solutions are needed, including strengthening privacy regulations, increasing corporate transparency in data use, and educating consumers and businesses. The implementation of regulations such as GDPR and CCPA shows important progress in the protection of personal data, but continuous efforts are needed to maintain consumer trust and ensure effective compliance worldwide.

References

Acquisti, A., John, L. K., & Loewenstein, G. (2013). What is Privacy Worth? The Journal of Legal Studies, 42(2), 249–274.

Binns, R. (2022). ‘Fairness in Machine Learning: Lessons from Political Philosophy’. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 149–159. https://doi.org/10.1145/3287560.3287586

Bygrave, L. A. (2014). Data Privacy Law: An International Perspective. Oxford University Press.

Cath, C. (2018). Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges. Philosophical Transactions of the Royal Society A, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080

Crawford, K. (2021). The Atlas of AI. Artificial Intelligence and Society, 36(3), 647–656. https://doi.org/10.1007/s00146-021-01131-3

DEWI, R. P. (2019). STUDI KASUS - METODE PENELITIAN KUALITATIF. Query date: 2024-05-25 20:59:55. https://doi.org/10.31227/osf.io/f8vwb

Gebru, T. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723

Iryana. (2019). Teknik Pengumpulan Data Metode Kualitatif. Query date: 2024-05-25 20:59:55. https://doi.org/10.31227/osf.io/2myn7

Jones, L. (2021). Renewable Energy Trends. Energy Perspectives, 15(1), 12–22. https://doi.org/10.1000/enp.2021.001

Leonard, K. A. (2020). Machine Learning and Bias: The Power of Data over Human Preferences, and How Achieving Fairness Requires Effort. Business Horizons, 63(6), 807–818. https://doi.org/10.1016/j.bushor.2020.07.010

Lyon, D. (2014). Surveillance, Snowden, and Big Data: Capacities, Consequences, Critique. Big Data & Society, 1(2), 2053951714541861. https://doi.org/10.1177/2053951714541861

Mittelstadt, B. (2019). Principles Alone Cannot Guarantee Ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4

Moha, I., & sudrajat, D. (2019). RESUME RAGAM PENELITIAN KUALITATIF. Query date: 2024-05-25 20:59:55. https://doi.org/10.31227/osf.io/wtncz

Naik, N. S. (2017). Computer Vision Uncovers Predictors of Physical Urban Change. Proceedings of the National Academy of Sciences, 114(29), 7571–7576. https://doi.org/10.1073/pnas.1619003114

Nissenbaum, H. (2010). Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press.

Noble, S. U. (2022). Decoding Algorithmic Bias: The Case for Critical Algorithm Studies. Journal of Digital Social Research, 4(1), 56–72. https://doi.org/10.33621/jdsr.v4i1.45

Pasquale, F. (2020). The Black Box Society: The Secret Algorithms That Control Money and Information. Science, Technology, & Human Values, 45(1), 150–160. https://doi.org/10.1177/0162243919832310

Richardson, R. T. (2021). Deep Learning with Synthetic Data: Accelerating Model Performance Evaluation in the Age of DeepFakes. Neural Networks, 135, 135–143. https://doi.org/10.1016/j.neunet.2021.02.010

Schwartz, P. M., & Solove, D. J. (2018). Information Privacy Law (6th, Ed.). Aspen Publishers.

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information Privacy: Measuring Individuals’ Concerns About Organizational Practices. MIS Quarterly, 20(2), 167–196.

Solove, D. J. (2008). Understanding Privacy. Harvard University Press.

Spiekermann, S., & Cranor, L. F. (2009). Engineering Privacy. IEEE Transactions on Software Engineering, 35(1), 67–82.

Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005

Warren, S. D., & Brandeis, L. D. (1890). The Right to Privacy. Harvard Law Review, 4(5), 193–220.

West, S. M. (2018). Censored, Suspended, Shadowbanned: Content Moderation and the Geography of Platform Governance in the United States. New Media & Society, 20(5), 2944–2963. https://doi.org/10.1177/1461444818786210

Zuboff, S. (2015). Big Other: Surveillance Capitalism and the Prospects of an Information Civilization. Journal of Information Technology, 30(1), 75–89. https://doi.org/10.1057/jit.2015.5

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Published

2025-03-22

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Vol. 3 No. 3 (2025): Maret

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