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Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
249-253

Differential privacy mixed attribute data publishing method for improved k-prototype clustering

Zhang Xing
Zhang Xing
School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0257

Abstract

Most of the current privacy protection methods in mixed attribute data publishing have problems of poor privacy protection effect or poor data utility. This paper proposed a differential privacy mixed attribute data publishing method(DCKPDP) based on improved k-prototype clustering. In order to solve the problem that the traditional k-prototype clustering algorithm did not consider the great influence of different numerical attributes on clustering results, using the information entropy added weight for each numerical attribute. In order to solve the problem of low accuracy of clustering results caused by artificial initial center points or randomly determined by random algorithm, combining the local density and high density of data objects carried out an adaptive selection of initial center points in the process of clustering. In order to solve the problem of high risk of data information leakage, using differential privacy protected the clustering center value. The experimental results show that the DCKPDP algorithm satisfies the requirement of differential privacy protection with less noise and better data availability.

Foundation Support

国家自然科学基金资助项目(61802161)
辽宁省教育厅科学研究经费资助项目(JZL202015402,JZL202015404)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0257
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Technology of Information Security
Pages: 249-253
Serial Number: 1001-3695(2022)01-044-0249-05

Publish History

[2021-10-09] Accepted Paper
[2022-01-05] Printed Article

Cite This Article

张星, 张兴. DCKPDP：改进k-prototype聚类的差分隐私混合属性数据发布方法 [J]. 计算机应用研究, 2022, 39 (1): 249-253. (Zhang Xing, Zhang Xing. Differential privacy mixed attribute data publishing method for improved k-prototype clustering [J]. Application Research of Computers, 2022, 39 (1): 249-253. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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