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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Privacy-preserving algorithm for clustering high-dimensional data based on random mapping

He Lili1,2,3
Zhang Chenglin1,2,3
Cao Mingzeng1,2,3
Zhang Lei1,2,3
1. School of Information & Electronics Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
2. The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence & Information Processing, School of Information Science & Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
3. Jiamusi Key Laboratory of Satellite Navigation Technology & Equipment Engineering Technology, Jiamusi 154007, Heilongjiang, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0503

Abstract

To address the challenge of increasing privacy costs with rising data dimensions in clustering privacy protection algorithms, introduces a privacy-preserving algorithm based on random projection (RPPP) . The algorithm selects relevant features using the symmetrical uncertainty method and generates random matrices through independently and identically distributed Gaussian sequences. To strengthen distance-preserving properties, it applies Gram-Schmidt orthogonalization to ensure the orthogonality of the random matrices. These matrices are then decomposed into multiple independent sub-matrices to map the reduced-dimensional features, creating a feature-matching domain and a noise-perturbed domain. To further enhance privacy protection, the algorithm injects random noise into the noise-perturbed domain. Experimental results demonstrate that RPPP effectively defends against privacy attacks. Tests conducted on the Cancer and Diabetes datasets reveal that RPPP outperforms traditional algorithms in both privacy protection and clustering efficiency. Specifically, RPPP improves clustering efficiency by approximately 16.34%, 23.44%, and 32.94% compared to UPA, GCCG, and AKA algorithms, respectively. Overall, RPPP significantly enhances privacy protection while boosting clustering efficiency, confirming its effectiveness and practical applicability.

Foundation Support

黑龙江省哲学社会科学研究规划项目(23GLD033)
黑龙江省自然科学基金联合引导项目(LH2021F054)
黑龙江省省属高等学校基本科研业务费优秀创新团队建设项目(2022-KYYWF-0654)
黑龙江省自主智能与信息处理重点实验室开放课题(ZZXC202302)
佳木斯大学国家基金培育项目(JMSUGPZR2022-014)
黑龙江省高等教育教学改革研究项目(SJGY20210873)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0503
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-20] Accepted Paper

Cite This Article

何丽丽, 张成林, 曹明增, 等. 基于随机映射的隐私保护聚类算法 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0503. (He Lili, Zhang Chenglin, Cao Mingzeng, et al. Privacy-preserving algorithm for clustering high-dimensional data based on random mapping [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0503. )

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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