ENHANCING PHARMACEUTICAL DATA SECURITY AND PRIVACY USING HOMOMORPHIC ENCRYPTION

Abstract

The rapid digital transformation of the healthcare and pharmaceutical industries has led to the generation and exchange of vast amounts of sensitive medical and pharmaceutical data. While cloud computing and data-sharing platforms offer significant benefits in terms of accessibility, storage, and collaborative research, they also introduce critical challenges related to data security, privacy, and regulatory compliance. Unauthorized access, data breaches, and privacy violations can compromise confidential patient information and intellectual property, thereby undermining trust in digital healthcare systems. To address these concerns, this study explores the use of Homomorphic Encryption (HE) as an advanced cryptographic technique for enhancing pharmaceutical data security and privacy. Homomorphic Encryption enables computations to be performed directly on encrypted data without requiring decryption, thereby preserving confidentiality throughout data processing operations. The proposed framework integrates homomorphic encryption mechanisms into pharmaceutical data management systems to ensure secure storage, transmission, and analysis of sensitive information. Furthermore, the study evaluates the effectiveness of homomorphic encryption in protecting pharmaceutical records while maintaining data usability and computational efficiency. Security analysis demonstrates that the proposed approach significantly reduces the risk of unauthorized data exposure and supports privacy-preserving data analytics. The framework also facilitates secure collaboration among healthcare providers, pharmaceutical companies, and research institutions without compromising sensitive information. The findings highlight the potential of homomorphic encryption as a reliable solution for safeguarding pharmaceutical data in modern healthcare environments. This research contributes to the advancement of secure healthcare information systems by providing a robust and privacy-preserving framework for pharmaceutical data management and analytics