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Title: Privacy-preserving analytics : secure logistic regression
Authors: Djonatan, Prabowo
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Abstract: Much data and information have been collected about us from all aspects of our life. Sometimes, we need to do analysis on this data without violating the privacy of individuals. In this project, we present a cryptographic library that can be used to do logistic regression under encrypted data. The encryption scheme used is a multiparty computation based on Exponential ElGamal. A special type of multiplication gate, the conditional gate, helps in the realization of the library. An implementation of the library usage on predicting the severity of heart disease based on the encrypted patient’s attributes is also presented along this project.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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