A Differential Privacy Random Forest Method of Privacy Protection in Cloud

被引:5
作者
Lv, Chaoxian [1 ]
Li, Qianmu [1 ]
Long, Huaqiu [2 ]
Ren, Yumei [3 ]
Ling, Fei [4 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Wuyi Univ, Intelligent Mfg Dept, Jiangmen, Peoples R China
[3] Jiangsu Womens Federation New Media & Network Inf, Nanjing, Jiangsu, Peoples R China
[4] Nanjing Liancheng Technol Dev Co Ltd, Jiangsu Postgrad Workstn, Nanjing, Jiangsu, Peoples R China
来源
2019 22ND IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (IEEE CSE 2019) AND 17TH IEEE INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (IEEE EUC 2019) | 2019年
关键词
privacy protection; differential privacy; random forest; ALGORITHM; QUERIES;
D O I
10.1109/CSE/EUC.2019.00093
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:470 / 475
页数:6
相关论文
共 50 条
  • [31] Random forest algorithm under differential privacy based on out-of-bag estimate
    Li Y.
    Chen J.
    Li Q.
    Liu A.
    Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2021, 53 (02): : 146 - 154
  • [32] Landmark Privacy: Configurable Differential Privacy Protection for Time Series
    Katsomallos, Manos
    Tzompanaki, Katerina
    Kotzinos, Dimitris
    CODASPY'22: PROCEEDINGS OF THE TWELVETH ACM CONFERENCE ON DATA AND APPLICATION SECURITY AND PRIVACY, 2022, : 179 - 190
  • [33] Protection of Privacy Data in a Cloud Environment
    Chen, Jeang-Kuo
    Chang, Fang-Sheng
    INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014), 2015, 274 : 964 - 971
  • [34] Continuous location privacy protection mechanism based on differential privacy
    Li H.
    Ren X.
    Wang J.
    Ma J.
    Tongxin Xuebao/Journal on Communications, 2021, 42 (08): : 164 - 175
  • [35] Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy
    Zhang, Jing
    Li, Yanzi
    Ding, Qian
    Lin, Liwei
    Ye, Xiucai
    ENTROPY, 2022, 24 (09)
  • [36] AdaBias: An Optimization Method With Bias Correction for Differential Privacy Protection
    Zhao, Xuanyu
    Hu, Tao
    Li, Jun
    Mao, Chunxia
    IEEE ACCESS, 2022, 10 : 107010 - 107021
  • [37] A Novel Protection Method of Continuous Location Sharing Based on Local Differential Privacy and Conditional Random Field
    Zhu, Linghe
    Hong, Haibo
    Xie, Mande
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT I, 2022, 13155 : 710 - 725
  • [38] An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services
    Wang, Bo
    Li, Hongtao
    Ren, Xiaoyu
    Guo, Yina
    SENSORS, 2023, 23 (11)
  • [39] Secure Random Sampling in Differential Privacy
    Holohan, Naoise
    Braghin, Stefano
    COMPUTER SECURITY - ESORICS 2021, PT II, 2021, 12973 : 523 - 542
  • [40] k Anonymous Trajectory Privacy Protection Scheme of Personalized Differential Privacy
    Song C.
    Cheng D.
    Ni S.
    Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2023, 46 (03): : 109 - 114