Privacy preserving based logistic regression on big data

Yongkai Fan, Jianrong Bai, Xia Lei, Yuqing Zhang, Bin Zhang, Kuan Ching Li, Gang Tan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Cloud computing has strong computing power and huge storage space. Machine learning algorithm, combining with cloud computing, makes the processing of large-scale data practical. Logistic regression algorithm is a widely popular machine learning-based classification algorithm that can be implemented in cloud. However, data privacy cannot be guaranteed in big data processing as privacy leakage of the training data may occur. In order to prevent the privacy leakage of logistic regression algorithm in the cloud and promote the processing efficiency of training data, this paper offers a Privacy Preserving Logistic Regression Algorithm (PPLRA). The homomorphic encryption is used to encrypt the private data when they are uploaded for training. Moreover, the approximation of the Sigmoid function in logistic regression using Taylor's theorem can support the safe calculation using homomorphic encryption. The Experimental results show that PPLRA has significant effects in data privacy preserving, and is more effective in data processing. Comparison with Non-Privacy Preserving Logistic Regression Algorithm (NPPLRA) shows that the computational efficiency is improved by about 1.2 times.

Original languageEnglish (US)
Article number102769
JournalJournal of Network and Computer Applications
Volume171
DOIs
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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