Privacy-Preserving Data Sharing Platform

Abstract

In today's data-driven healthcare landscape, the secure sharing of sensitive medical information is essential for improving patient care, facilitating medical research, and advancing healthcare outcomes. However, ensuring the integrity, confidentiality, and privacy of patient data poses significant challenges, particularly in the context of big data environments. This presents a comprehensive framework for privacy-preserving data sharing in healthcare, leveraging a combination of cryptographic techniques, encryption, and secure computation protocols. The framework encompasses various privacy-preserving mechanisms, including Differential Privacy with Data Perturbation, Secure Multi-Party Computation (SMPC), and Homomorphic Encryption, to protect sensitive healthcare data from unauthorized access and disclosure. By implementing state-of-the-art privacy-preserving techniques, the framework aims to enable secure data sharing among multiple parties while complying with regulatory requirements such as HIPAA and GDPR. Additionally, the paper discusses the project scope, which includes cryptography, encryption, decryption, integrity, confidentiality, privacy, policies, procedures, security, and secure data sharing infrastructure. The proposed framework provides a practical solution for healthcare organizations and research institutions to collaborate on data-driven initiatives while safeguarding patient privacy and maintaining trust. Evaluation of the framework's effectiveness and performance metrics is conducted to validate its feasibility and efficacy in real-world healthcare settings. Keywords: Privacy-preserving data sharing, Differential Privacy, Data Perturbation, Secure Multi-Party Computation (SMPC)