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To improve patient outcomes and advance medical research, it is crucial that healthcare data are shared securely and effectively. It is difficult to communicate data while retaining confidentiality, though, due to the delicate nature of healthcare data and worries about patient privacy. Sensitive patient data must be safeguarded while facilitating secure data sharing, which calls for a framework that protects privacy. For implementing such a system, various methods like differential privacy, secure multi-party computation, and homomorphic encryption have been suggested. It is necessary to handle issues including interoperability problems, legal and ethical dilemmas, and technical difficulties. In order to create a framework for sharing medical data while protecting privacy, this study suggests a method that combines homomorphic encryption and blockchain technology. The suggested method offers a safe and effective means of exchanging and storing encrypted healthcare data while preserving data integrity and privacy. A multidisciplinary strategy combining cooperation between healthcare providers, data scientists, privacy experts, and regulatory agencies is necessary for the development and implementation of a medical data sharing framework that protects patient privacy. A privacy-preserving framework for medical data sharing can promote effective and secure data exchange for better healthcare outcomes by addressing the issues and utilizing the tools at hand.

Due to the fast development of internet services and a huge amount of network traffic, it is becoming an essential issue to reduce World Wide Web user-perceived latency. Although web performance is improved by caching, the benefit of caches is limited. To further reduce the retrieval latency, web prefetching becomes an attractive solution to this problem. In this work, we introduce a simple and transparent popularity-based prefetching algorithm which combines both the top 10 and next-n prefetching approaches. In addition to using access-frequency as the criteria for prefetching, we also use the time of access of web documents to generate the top 10 list. This approach of using accessfrequency and time of access is known as the GDSP approach, which has been used in cache management. Instead of generating next-n list for all the documents accessed by the users, we log the next-n documents for the top 10 documents only, thus reducing complexity and overhead. The results obtained from algorithm, in terms of hit rate and prefetching effectiveness show the efficacy of our proposed algorithm as compared to other approaches.

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