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Privacy preserving dynamic data release against synonymous linkage based on microaggregation.

Yan Yan1, Anselme Herman Eyeleko2, Adnan Mahmood3

  • 1School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.

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This study introduces a novel privacy-preserving method for dynamic data publishing using microaggregation. It effectively reduces privacy risks from non-numerical data, enhancing data utility and protection.

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Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • The proliferation of mobile internet and big data increases personal information digitization risks.
  • Existing privacy-preserving methods struggle with semantic relevance in non-numerical data, leading to potential privacy breaches.
  • Anonymization models often fail to address synonymous linkages in sensitive non-numerical information.

Purpose of the Study:

  • To propose a novel privacy-preserving dynamic data publishing method.
  • To address the challenge of semantic relevance and synonymous linkages in non-numerical sensitive data.
  • To enhance the effectiveness and utility of privacy protection in big data publishing.

Main Methods:

  • Development of a privacy-preserving dynamic data publishing method based on microaggregation.
  • Design of indicators to evaluate synonymous linkages between non-numerical sensitive values.
  • Integration of a dynamic update program for real-time data release and modification.

Main Results:

  • The proposed microaggregation method improves clustering by evaluating synonymous linkages.
  • The dynamic update program enables efficient and timely data release.
  • Experimental results demonstrate superior privacy protection and data availability compared to existing methods.

Conclusions:

  • The microaggregation-based method offers enhanced privacy protection for dynamic data publishing.
  • The approach effectively mitigates privacy risks associated with non-numerical sensitive data.
  • The method balances robust privacy preservation with practical data availability for big data applications.