Work place: Department of Industrial Engineering, Sakarya University, Sakarya, Turkey
Research Interests: Computer systems and computational processes, Information Systems, Decision Support System, Logic Calculi, Logic Circuit Theory
Safiye Turgay received her undergraduate degree from Istanbul Technical University, department of Industrial Engineering, master's and doctorate degrees from Sakarya University’s department of Industrial Engineering from the Institute of Natural and Applied Sciences. She worked as a lecturer Bolu Abant ˙Izzet Baysal University Computer Programming, Computer and Teaching Technologies and Education, Business Administration, Sakarya University Management Information Systems. She is currently Associate Professor in the Sakarya University, Faculty of Engineering, Department of Industrial Engineering. She has many publications on multi-agent systems, fuzzy logic, decision support systems, production systems, multi-criteria decision making techniques and rough sets.
DOI: https://doi.org/10.5815/ijisa.2022.05.01, Pub. Date: 8 Oct. 2022
Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined.
Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.
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