Collaborative Intelligent Computing Model for Privacy-Preserving Hybrid Cloud Organizational Linkages

Abstract

The increasing adoption of hybrid cloud infrastructures in modern organizations has introduced significant challenges in achieving secure, efficient, and intelligent system integration. Enterprises rely on heterogeneous computing environments that combine public cloud services, private infrastructures, and edge computing nodes. While these architectures provide scalability and flexibility, they also expose critical vulnerabilities related to data privacy, interoperability, and collaborative processing. This paper proposes a collaborative intelligent computing model designed to enable privacy-preserving organizational linkages across hybrid cloud environments.

The proposed model integrates distributed intelligence, service-oriented architecture principles, and privacy-preserving computation mechanisms to facilitate secure data sharing and coordinated decision-making among interconnected organizational systems. By leveraging decentralized computing strategies and collaborative machine learning techniques, the model ensures that sensitive data remains localized while enabling cross-platform intelligence generation. The framework incorporates adaptive communication protocols, secure aggregation mechanisms, and intelligent orchestration layers to enhance system resilience and performance.

A comprehensive analysis of existing literature reveals that current approaches either emphasize distributed computation or focus on system integration, with limited attention to unified frameworks that address both aspects simultaneously. Studies on service-oriented architectures (ENDREI et al., 2004; Yang et al., 2010) provide foundational insights into system interoperability, while research on decentralized and edge computing models highlights the importance of distributed intelligence (Zangana et al., 2024). Furthermore, domain-specific applications such as intelligent traffic management and maritime systems demonstrate the practical need for collaborative computing frameworks (Xi Yu et al., 2012; Akdağ et al., 2022).

The findings indicate that the proposed model significantly enhances data privacy, reduces communication overhead through intelligent coordination, and improves interoperability across hybrid cloud platforms. However, challenges related to system complexity, synchronization delays, and heterogeneous infrastructure management remain critical considerations.

This research contributes to the advancement of intelligent distributed systems by providing a comprehensive, scalable, and privacy-aware computing model. It establishes a foundation for future research in hybrid cloud intelligence, particularly in the context of secure organizational collaboration and adaptive system integration.