Smart Health Prediction Using Multiplicative Homomorphic Encryption Algorithm
Author(s):
SENTHILNATHAN M, PRAVEENKUMAR B, SUBBULAKSHMI S
Keywords:
Apriori algorithm, Middle ware technology, multiplicative homomorphic encryption, clinical decision support
Abstract
A clinical decision support system forms a critical capability to link health observations with health data to influence choices by clinicians for improved healthcare. Recent trends towards remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health facts located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not entirely trusted raises possible privacy concerns. The proposed paper states a novel privacy-preserving protocol for a clinical decision support system where the patients’ data always stay in encrypted form during the diagnosis process. Hence the server involved in the diagnosis process is not able to learn any extra knowledge about the patient data and results. The health prediction system is an end user support and online consultation project. The system is fed with symptoms, which is the patient context, and the context is encrypted using multiplicative homomorphic encryption technique, Data mining techniques like Apriori algorithm are used to guess the most accurate illness for the appropriate symptoms for each patient. To use the system in varied network bandwidth, and to ensure adaptability some middleware technologies like tuple-space, context-ware, event-based and relative methodology are used. Patient can inquiry with the expert for any doubts, which is also secured. The middleware technology ensures less network fluctuation and makes to use in an ubiquitous environment. If patients have any query about the minor health problem , they will send the query to the expert.
Article Details
Unique Paper ID: 144322

Publication Volume & Issue: Volume 3, Issue 10

Page(s): 139 - 142
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