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Clinical Decision Support System for the Patients Efficientness in privacy Preserving Way with Naïve Bayesian Classification

Author(s):

Manodnya A. Shitole , AVCOE,Sangamner; Prof. M. A. Wakchaure, AVCOE,Sangmner

Keywords:

Privacy Preserving, Patient centric, Cryptography, Homomorphic aggregation scheme, authorization etc

Abstract

Statistics from security firms, research institutions and government organizations show that the number of data-leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy- preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semi honest provider without revealing the sensitive data to the provider.So in the proposed system the patient security is the main part and in that provided the security to the patient by giving the restriction to the doctor accession. In that we check the authorization of the doctor with the OTP generation because of that the data is preserved. And also the effective Naive Bayesian classification use for the patient easiness for getting the results from the doctor about the disease diagnosis also one prominent part provided in this that patient can upload the document of their so doctor will get help to diagnosis the patient.

Other Details

Paper ID: IJSRDV4I70626
Published in: Volume : 4, Issue : 7
Publication Date: 01/10/2016
Page(s): 999-1003

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