Privacy Preserving Computational Models for Secure Multi Party Data Analytics in Cloud Native Environments
Keywords:
Privacy-preserving computation, Multi-party data analytics, Cloud-native, Homomorphic encryption, Secure multiparty computation, Differential privacyAbstract
Privacy-preserving computational models have emerged as a critical paradigm for enabling secure multi-party data analytics in cloud-native environments. As modern organizations increasingly rely on distributed and federated data ecosystems, the need for robust methods to ensure data confidentiality while supporting large-scale computations is paramount. This paper explores state-of-the-art privacy-preserving frameworks, including homomorphic encryption, secure multiparty computation, and differential privacy, with a focus on their adaptation to cloud-native architectures. We evaluate their scalability, efficiency, and applicability in multi-tenant infrastructures. A conceptual flow model is proposed to integrate privacy-preserving computation into Kubernetes-based microservices. Experimental insights from published literature are consolidated to highlight challenges, trade-offs, and future research directions in the domain.
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