BCP-based joint delegation learning model and protocol

被引：0
|
作者：
Gao S. [1 ,2 ]
Xiang K. [1 ,3 ]
Tian Y. [1 ,3 ]
Tan W. [1 ]
Feng T. [4 ]
Wu X. [5 ]
机构：
[1] State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang
[2] School of Information, Central University of Finance and Economics, Beijing
[3] Institute of Cryptography and Data Security, Guizhou University, Guiyang
[4] School of Computer and Communication, Lanzhou University of Technology, Lanzhou
[5] Institute for Metrology and Calibration of Guizhou, Guiyang
来源：
Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 05期
基金：
中国国家自然科学基金;
关键词：
BCP homomorphic encryption; Data mining; Decision tree; Delegation learning;
D O I：
10.11959/j.issn.1000-436x.2021089
中图分类号：
学科分类号：
摘要：
In order to realize data security sharing and reduce the computing costs of clients in data mining process, a joint delegation learning model and protocol based on BCP homomorphic encryption algorithm was proposed. Firstly, a privacy preserving method based on false records was proposed for the security of decision tree model. Secondly, in view of the vertical and horizontal distribution of data, the corresponding delegation learning protocols based on privacy preserving delegation dot product algorithm and privacy preserving delegation entropy algorithm was proposed. Finally, the security proof and the performance analysis of delegation learning protocols and decision tree model structure were given. The results show that the privacy protection method based on false records does not affect the final model construction, and the final model obtained by each client is the same as that constructed by real data. © 2021, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：137 / 148
页数：11
相关论文
共 23 条
  • [1] JIN H, LUO Y, LI P L, Et al., A review of secure and privacy-preserving medical data sharing, IEEE Access, 7, pp. 61656-61669, (2019)
  • [2] SUN Y, YIN L H, SUN Z, Et al., An IoT data sharing privacy preserving scheme, IEEE INFOCOM 2020-IEEE Conference on Computer Communi-cations Workshops, pp. 984-990, (2020)
  • [3] ZHAO C, ZHAO S N, ZHAO M H, Et al., Secure multi-party computation: theory, practice and applications, Information Sciences, 476, pp. 357-372, (2019)
  • [4] VU D H, LUONG T D, HO T B., An efficient approach for secure multi-party computation without authenticated channel, Information Sciences, 527, pp. 356-368, (2020)
  • [5] REDDY G T, REDDY M P K, LAKSHMANNA K, Et al., Analysis of dimensionality reduction techniques on big data, IEEE Access, 8, pp. 54776-54788, (2020)
  • [6] WANG C, WANG A D, XU J, Et al., Outsourced privacy-preserving decision tree classification service over encrypted data, Journal of Information Security and Applications, 53, (2020)
  • [7] OLAKANMI O O, DADA A., An efficient privacy-preserving approach for secure verifiable outsourced computing on untrusted platforms, International Journal of Cloud Applications and Computing, 9, 2, pp. 79-98, (2019)
  • [8] MANIKANDAN V, PORKODI V, MOHAMMED A S, Et al., Privacy preserving data mining using threshold based fuzzy c-means clustering, ICTACT Journal on Soft Computing, 9, 1, pp. 1813-1816, (2018)
  • [9] TEO S G, CAO J, LEE V C S., DAG: a general model for privacy-preserving data mining, IEEE Transactions on Knowledge and Data Engineering, 32, 1, pp. 40-53, (2018)
  • [10] AGRAWAL R, SRIKANT R., Privacy-preserving data mining, 2000 ACM SIGMOD International Conference on Management of Data, pp. 439-450, (2000)
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