PhilArchive
open access
Privacy-Aware Architectures for Cloud-Native Applications: A Systematic Review
Cloud-native architectures have fundamentally transformed application development, offering unprecedented scalability and operational efficiency. However, these distributed, ephemeral systems introduce complex privacy challenges that demand rigorous examination. This systematic review analyzes user data privacy mechanisms, vulnerabilities, and protection strategies in cloud-native applications across major cloud providers based on 36 peer-reviewed sources published between 2020-2025. We identify critical privacy challenges including multitenancy vulnerabilities, ephemeral infrastructure complications, and regulatory compliance complexities. Our analysis reveals that while cloud providers offer comprehensive security controls, privacy-specific mechanisms remain underdeveloped, with industry reports estimating that only 25 to 40% of implementations demonstrate strong privacy protections beyond basic encryption. We propose a layered privacy architecture incorporating Zero Trust principles, privacy-enhancing technologies, and adaptive policy enforcement to address these gaps. Our findings provide actionable insights for organizations deploying cloud-native applications while maintaining robust privacy guarantees.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Next-Generation Cloud Security Frameworks:Balancing Privacy, Compliance, and Data Protection in a Digital-First Era#70881
- Data-driven Technologies – Challenges for Privacy and EU Data Protection Law#211765
- Cloudshield: The Future of Cloud Security#101670
- Privacy-preserving federated machine learning on FAIR health data: A real-world application#404537
- Privacy-preserving federated machine learning on FAIR health data: A real-world application#177316
Record · ID 84512
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive