App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Noise statistical constraint-based dynamic pruning for streaming data mining under local differential privacy

Jiao Jingzhea
Li Xiaohuia
Deng Qiana
Lan Jieb
Xue Xiaoronga
a. School of Electronics & Information Engineering, b. College of Science, Liaoning University of Technology, Jinzhou Liaoning 121000, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0181

Abstract

Streaming frequent itemset mining under local differential privacy faces computational redundancy and memory expansion problems. This paper proposed a streaming mining algorithm based on noise statistical constraints. The algorithm designed a concept-drift-state-driven dual-mode privacy budget allocation mechanism. The algorithm utilized a differential update mechanism to achieve sliding window state reuse. This step eliminated recalculation overhead. The method constructed a noise constraint interval to accurately distinguish between random perturbations and real concept drifts. The algorithm applied a prefix tree physical pruning strategy to release invalid patterns in real time. The experimental results showed that the algorithm effectively mitigated the difficulty of balancing high accuracy, low memory consumption, and high throughput. The algorithm achieved more than a 2.5-fold spatial efficiency optimization compared to similar baselines. The algorithm significantly improves the comprehensive mining utility and stability in high-speed data stream scenarios.

Foundation Support

国家自然科学基金资助项目(62203201)
2024年辽宁省属本科高校基本科研业务费专项资金资助项目(LJZZ212410154025)
2026年辽宁省应用基础研究项目(2026JH2/101300073)
辽宁省教育厅科研项目(LJZZ212410154029)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0181
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-23] Accepted Paper

Cite This Article

焦敬哲, 李晓会, 邓倩, 等. 噪声统计约束的本地差分隐私动态剪枝流式挖掘方法 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0181. (Jiao Jingzhe, Li Xiaohui, Deng Qian, et al. Noise statistical constraint-based dynamic pruning for streaming data mining under local differential privacy [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0181. )

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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