Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) for Data Privacy in the Context of Big Data

• Main Authors: Chen, Q. (Author), Deng, M. (Author)
• Format: Article
• Language: English
• Published: IOP Publishing Ltd 2021
• Subjects: Big data; Clustering algorithms; 'current; Data privacy; Encryption protection; Encryption technologies; Homomorphic encryption; Ho-momorphic encryptions; Homomorphic-encryptions; Logistic regression; Logistic regression algorithms; Machine learning; Privacy preserving; Privacy protection; Privacy-preserving techniques; Protection methods; Regression algorithms
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 17426588 (ISSN)
• DOI: 10.1088/1742-6596/2083/3/032059

Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied.This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm. © 2021 Institute of Physics Publishing. All rights reserved.