PREDICTIVE ANALYSIS ON HOMOMORPHICALLY ENCRYPTED MEDICAL DATA
Author(s):
ANITHA KUMARI K, KIRTHIKA S
Keywords:
Homomorphic encryption, Predictive analysis, encrypted medical data, Logistic regression, Gorti’s Enhanced Homomorphic Cryptosystem (EHC).
Abstract
Data security challenges faced by healthcare sector are increasing day by day. To ensure the security and privacy of data, fully homomorphic encryption schemes can be applied. To make the schemes suitable for applying in real time, light-weight state-of-art homomorphic encryption scheme like Gorti’s Enhanced Homomorphic Cryptosystem (EHC) scheme is proposed. Predictive analysis is used for prediction of occurrence of diabetes in future. And to reduce the noise rate, modulus switching technique is proposed. Modulus switching technique manages noise by scaling the size of modulus. The depth of leveled computational circuits in the form of polynomial are predefined prior to the start of computation. The bound on length is known to the evaluator while the secret key is not known. The cipher text c modulo p is transformed in to a different cipher text modulo q. This transformation of cipher text leads to simple scaling and approximate rounding, thereby maintaining value of q sufficiently smaller that of p, noise can be reduced.
Article Details
Unique Paper ID: 149587

Publication Volume & Issue: Volume 7, Issue 1

Page(s): 153 - 162
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Volume 7 Issue 1

Last Date 25 June 2020


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