PRIVACY PRESERVING DATA SHARING CLOUD-BASED HEALTHCARE SYSTEMS

Abstract

The integration of cloud computing into healthcare systems has revolutionized data management by enabling seamless data sharing and enhanced accessibility, thereby improving patient outcomes and operational efficiency. However, the sensitive nature of healthcare data necessitates robust privacy-preserving mechanisms to protect patient confidentiality. This paper explores state-of-the-art techniques such as cryptography, data anonymization, and secure access control, which are tailored to address privacy challenges in healthcare environments. It also critically evaluates existing frameworks, identifying limitations, and proposes a scalable architecture designed to ensure secure and efficient data sharing. Healthcare systems increasingly rely on cloud environments for storing and sharing sensitive patient data. However, these systems face significant challenges, including the risk of data breaches, unauthorized access, and the need for strict compliance with regulatory frameworks such as HIPAA and GDPR. To address these challenges, this paper presents a privacy-preserving framework that leverages advanced cryptographic methods, blockchain for secure transaction logging, and machine learning-based anomaly detection to safeguard patient data from collection to sharing. Experimental results demonstrate that the proposed system outperforms traditional methods in terms of privacy, efficiency, and scalability, showcasing its potential for real-world healthcare applications. The integration of cloud computing into healthcare systems has significantly transformed how patient data is managed and shared across multiple stakeholders, including healthcare providers, insurers, and researchers. This digital transformation has enabled real-time access to medical records, improving clinical decision-making and patient outcomes. However, the sensitive nature of healthcare data demands the adoption of advanced privacy-preserving techniques to mitigate risks such as unauthorized access, data breaches, and regulatory non-compliance. In this paper, we explore state-of-the-art technologies like cryptographic techniques (homomorphic encryption, differential privacy), data anonymization (k-anonymity, l-diversity), and access control mechanisms (RBAC, ABAC) that have been proposed to enhance data privacy and security. Additionally, we delve into the use of blockchain technology for secure, transparent transaction logging and machine learning-based anomaly detection systems to safeguard against unauthorized access. Through experimental analysis and performance testing, we demonstrate that the proposed privacy-preserving framework outperforms conventional methods in terms of scalability, efficiency, and data protection, thereby offering a promising solution for real-world healthcare applications.