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01-12-2019 | Research | Issue 1/2019 Open Access

Human-centric Computing and Information Sciences 1/2019

Local privacy protection classification based on human-centric computing

Journal:
Human-centric Computing and Information Sciences > Issue 1/2019
Authors:
Chunyong Yin, Biao Zhou, Zhichao Yin, Jin Wang
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Abstract

Human-centric computing is becoming an important part of data-driven artificial intelligence (AI) and the importance of data mining under Human-centric computing is getting more and more attention. The rapid development of machine learning has gradually increased its ability to mine data. In this paper, privacy protection is combined with machine learning, in which a logistic regression is adopted for local differential privacy protection to achieves classification task utilizing noise addition and feature selection. The design idea is mainly divided into three parts: noise addition, feature selection and logistic regression. In the part of noise addition, the way of adding noise using Laplace mechanism to original data achieves the purpose of disturbing data. The part of feature selection is to highlight the impact of noised data on the classifier. The part of logistic regression is to use logistic regression to implement classification task. The experimental results show that an accuracy of 85.7% can be achieved for the privacy data by choosing appropriate regularization coefficients.

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