A FRAMEWORK FOR HEALTH INFORMATION SHARING AND PRIVACY PRESERVATION USING INFORMATION HIDING AND DIFFERENTIAL PRIVACY.

Abstract

Health information exchanges (HIEs) are an important part of the healthcare system. The World Health Organization emphasizes the importance of security standards in managing personal health data, as privacy concerns among healthcare consumers are increasing. HIPAA prioritizes unauthorized disclosures, improper disposal, access, and other IT security and privacy breaches of protected health information (PHI). The study demonstrates secure health information sharing methods, ensuring trust and patient privacy through AES-128-CBC and VSS-XOR, and integrating authentication, authorization, and access control with attributes for user tracking. We perform further comparative analyses based on the visual cryptography in LSB and DWT algorithms. PSNR results describe the similarity between the recovered secret image and the original image. The novel health data sharing method using Named Entity Recognition (NER), NER BiLSTM, provides 94% more accurate PII prediction than current evidence on medical text prediction. The differential privacy application evaluates noise levels to protect patients' privacy by removing or replacing patients and was compared using word2vec and GloVe 300-dimensional embedding approaches. Word2vec performed better semantically, while GloVe outperformed due to its support for a wide range of epsilon values, ensuring patient privacy. The effectiveness and viability of the suggested scheme are demonstrated by experiments and comparisons of the metrics. It is demonstrated that our suggested scheme can withstand statistical analysis and counterattacks. By combining legal agreements and technology to ensure meaningful information exchange, our developed protocol complied with information security and HIPPA privacy basic rules