| Title | A Differential Privacy Random Forest Method of Privacy Protection in Cloud |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lv, Chaoxian, Li, Qianmu, Long, Huaqiu, Ren, Yumei, Ling, Fei |
| Conference Name | 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) |
| Date Published | aug |
| Keywords | classification accuracy, Classification algorithms, cloud computing, composability, data privacy, Decision trees, Differential privacy, differential privacy protection, differential privacy random forest method, Forestry, high classification performance, high privacy protection, Human Behavior, hybrid decision tree algorithm, pattern classification, Prediction algorithms, privacy, privacy protection, pubcrawl, Random Forest, random forest algorithm, random forest classification algorithm, Resiliency, Scalability |
| Abstract | This paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value. |
| DOI | 10.1109/CSE/EUC.2019.00093 |
| Citation Key | lv_differential_2019 |
A Differential Privacy Random Forest Method of Privacy Protection in Cloud