Privacy-Preserving Enterprise Intelligence Through Secure Cloud Data Governance and Distributed Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0804017Keywords:
Privacy-Preserving Analytics, Enterprise Intelligence, Cloud Data Governance, Distributed Analytics, Data Security, Federated Learning, Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation, Regulatory Compliance, Cloud Computing, Data PrivacyAbstract
The rapid growth of cloud computing and data-driven decision-making has transformed enterprise intelligence by enabling organizations to collect, store, and analyze vast amounts of information. However, concerns regarding data privacy, security, regulatory compliance, and unauthorized access continue to challenge enterprises operating in cloud environments. Privacy-preserving enterprise intelligence aims to balance the extraction of valuable insights with the protection of sensitive organizational and customer data. This research explores the integration of secure cloud data governance and distributed analytics as a framework for achieving privacy-preserving intelligence. Secure cloud data governance establishes policies, access controls, encryption mechanisms, and compliance procedures that ensure data integrity, confidentiality, and accountability. Distributed analytics enables data processing across decentralized environments without requiring the movement of sensitive information to a centralized repository, thereby reducing privacy risks. The study examines contemporary technologies such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation that support privacy-preserving analytics. Furthermore, the research evaluates the effectiveness of governance models and distributed analytical architectures in improving enterprise intelligence while maintaining regulatory compliance and user trust. The findings indicate that combining secure cloud governance with distributed analytics significantly enhances organizational decision-making capabilities while minimizing privacy threats, making it a critical approach for modern enterprises seeking sustainable digital transformation.
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