Beyond GDPR: The Architectural Challenge of Data Sovereignty and Confidential Computing in the Post-2024 Era
DOI:
https://doi.org/10.5281/zenodo.19918374Keywords:
Data Sovereignty, DPDP Act 2023, Confidential Computing, Federated Learning, GDPR, Cloud Security, Big Data Governance.Abstract
As organizations migrate legacy datasets to cloud-native architectures, the tension between Big Data analytics and data privacy regulations has reached a critical inflection point. With the full operationalization of India’s Digital Personal Data Protection (DPDP) Act in 2025 and the tightening of GDPR enforcement, the concept of "Data Sovereignty" has evolved from a legal footnote to a primary architectural constraint. This paper reviews the limitations of traditional "encryption-at-rest" standards in the face of these new laws. We analyze emerging solutions, specifically Confidential Computing (using hardware-based Trusted Execution Environments) and Federated Learning, which promise to decouple data processing from data visibility. Market analysis suggests the Confidential Computing sector alone will expand to over USD 14 billion by late 2025. We argue that the future of software engineering lies not in centralized data lakes, but in decentralized, privacy-preserving compute fabrics.
References
Government of India, "The Digital Personal Data Protection Act, 2023," The Gazette of India, Aug. 2023. (Notified Rules 2025).
Precedence Research, "Confidential Computing Market Size to Hit USD 1281.26 Bn by 2034," Global Market Insights, Jan. 2025.
S. Saha et al., "A multifaceted survey on privacy preservation of federated learning: Progress, challenges, and opportunities," Artificial Intelligence Review, vol. 57, no. 1, 2024.
H. Schwarz, "Comprehensive Review on Privacy-Preserving Machine Learning Techniques," Edu Journal of International Affairs and Research, vol. 3, no. 2, 2024.
Endor Labs, "The Most Common Security Vulnerabilities in AI-Generated Code," Endor Labs Research Blog, Aug. 2025.
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Copyright (c) 2025 Mr. Tayabur Rahman Laskar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright. Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ (CC BY 4.0 deed)
