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Related Experiment Video

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Data block decomposition and intelligent secure acquisition of microdata.

Xiuquan Zhang1, Lin Shen2, Kaiquan Shi3

  • 1School of Mathematics and Statistics, Huanghuai University, Zhumadian, China. zhangxiuquan@huanghuai.edu.cn.

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|April 4, 2023
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Summary
This summary is machine-generated.

This study introduces P-sets, a dynamic mathematical model, to analyze Class I big data. P-sets offer new theoretical and application methods for secure microdata discovery and acquisition.

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Area of Science:

  • Mathematics
  • Computer Science
  • Data Science

Background:

  • Class I big data exhibits dynamic characteristics.
  • Existing models may not fully capture these dynamic traits.
  • A novel mathematical framework is needed for efficient big data analysis.

Purpose of the Study:

  • To introduce P-sets (Packet sets) as a dynamic mathematical model for Class I big data.
  • To develop new theoretical concepts and algorithms for microdata analysis.
  • To present applications in secure microdata acquisition.

Main Methods:

  • Enhancement of the Cantor set to create P-sets with dynamic characteristics.
  • Introduction of new definitions: data block, microdata, and data link.
  • Development of theorems: data block decomposition, microdata relation, and attribute reasoning.
  • Proposal of an intelligent secure acquisition algorithm for microdata.

Main Results:

  • P-sets demonstrate consistency with Class I big data characteristics.
  • New theorems provide a theoretical foundation for microdata analysis.
  • An algorithm for intelligent and secure microdata acquisition is presented.

Conclusions:

  • The P-sets mathematical model offers a novel approach to studying Class I big data.
  • This framework facilitates theoretical advancements and practical applications in data analysis.
  • The research contributes to the secure and intelligent handling of big data.