Exploring Privacy-Preserving Disease Diagnosis: A Comparative Analysis

In the healthcare sector, data is considered as a valuable asset, with enormous amounts generated in the form of patient records and disease-related information. Leveraging machine learning techniques enables the analysis of extensive datasets, unveiling hidden patterns in diseases, facilitating personalized treatments, and forecasting potential health issues. However, the flourish of online diagnosis and prediction still faces some challenges related to information security and privacy as disease diagnosis technologies utilizes a lot of clinical records and sensitive patient data. Hence, it becomes imperative to prioritize the development of innovative methodologies that not only advance the accuracy and efficiency of disease prediction but also ensure the highest standards of privacy protection. This requires collaborative efforts between researchers, healthcare practitioners, and policymakers to establish a comprehensive framework that addresses the evolving landscape of healthcare data while safeguarding individual privacy. Addressing this constraint, numerous researchers integrate privacy preservation measures with disease prediction techniques to develop a system capable of diagnosing diseases without compromising the confidentiality of sensitive information. The survey paper conducts a comparative analysis of privacy-preserving techniques employed in disease diagnosis and prediction. It explores existing methodologies across various domains, assessing their efficacy and trade-offs in maintaining data confidentiality while optimizing diagnostic accuracy. The review highlights the need for robust privacy measures in disease prediction, shortcomings related to existing techniques of privacy preserving disease diagnosis, and provides insights into promising directions for future research in this critical intersection of healthcare and privacy preservation

Secure and Trustable Electronic Medical Records Sharing using Blockchain

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding