Improving healthcare services using source anonymous scheme with privacy preserving distributed healthcare data collecti
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Improving healthcare services using source anonymous scheme with privacy preserving distributed healthcare data collection and mining Nikunj Domadiya1
· Udai Pratap Rao1
Received: 12 April 2020 / Accepted: 28 September 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.
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Nikunj Domadiya [email protected] Udai Pratap Rao [email protected]
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Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
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N. Domadiya, U. P. Rao
Keywords Healthcare · Data Mining · Privacy · Source Anonymous · Privacy Preserving Data Mining · Healthcare Improvement Mathematics Subject Classification 68P20 · 68P27 · 92C50
1 Introduction In this digital world, data analysis becomes an important research area in major domains like healthcare, banking, education, business, etc. for improving some services related to a specific domain. Data mining techniques(e.g. Association Rule Mining, Classification, Clustering, etc.) are majorly used for analyzing the data to extract the hidden patterns. Major private or government organizations of the same or different domains used to collaborate to perform the data mining on combined data from all collaborative organizations (or participants) to ext
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