Privacy Preserving Distributed AI Analytics for Secure Financial and Healthcare Cloud Infrastructure

Abstract

The rapid adoption of cloud computing in financial and healthcare sectors has significantly enhanced data accessibility, scalability, and computational capabilities. However, these benefits also introduce critical challenges related to data privacy, security, and regulatory compliance. Sensitive information such as financial transactions, patient health records, and personal identification data must be protected from unauthorized access while still enabling advanced analytics and intelligent decision-making. This research proposes a Privacy-Preserving Distributed Artificial Intelligence (AI) Analytics framework designed to secure financial and healthcare cloud infrastructures while maintaining efficient data processing and collaborative intelligence. The framework integrates distributed AI models with privacy-preserving techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy. These techniques enable organizations to train AI models collaboratively without sharing raw sensitive data across cloud environments. The proposed architecture ensures data confidentiality, reduces the risk of data breaches, and enhances trust among participating institutions. The research methodology involves the design of a distributed cloud-based architecture, implementation of privacy-preserving machine learning algorithms, and performance evaluation using simulated financial and healthcare datasets. Experimental results demonstrate that the framework maintains strong privacy protection while achieving high predictive accuracy and scalable analytics performance. The proposed approach contributes to the development of secure and intelligent cloud infrastructures capable of supporting sensitive data analytics in highly regulated industries.