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An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques.

Can Eyupoglu1, Muhammed Ali Aydin2, Abdul Halim Zaim1

  • 1Department of Computer Engineering, Istanbul Commerce University, Istanbul 34840, Turkey.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces a novel chaos and perturbation algorithm for big data anonymization, enhancing privacy while preserving data utility. The method demonstrates superior performance in key metrics compared to existing approaches.

Keywords:
big datachaosdata anonymizationdata perturbationprivacy preserving

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Area of Science:

  • Computer Science
  • Data Privacy
  • Information Security

Background:

  • Big data presents significant challenges for data privacy and protection models.
  • Maintaining data usability during sharing and processing is crucial.
  • Data anonymization is essential to prevent identity disclosure and linking attacks.

Purpose of the Study:

  • To propose a novel data anonymization algorithm for privacy and utility preservation in big data.
  • To address the challenge of protecting sensitive information while ensuring data usability.
  • To enhance security in big data environments.

Main Methods:

  • Development of a new data anonymization algorithm utilizing chaos theory and data perturbation.
  • Evaluation of the algorithm's performance using metrics like Kullback-Leibler divergence and probabilistic anonymity.
  • Comparative analysis against existing anonymization techniques on the same datasets.

Main Results:

  • The proposed algorithm demonstrates efficiency in big data anonymization.
  • It shows improved performance in Kullback-Leibler divergence, classification accuracy, and F-measure.
  • Outperforms many existing algorithms in privacy and utility preservation.

Conclusions:

  • The chaos-based perturbation algorithm is effective for privacy-preserving big data mining and publishing.
  • It offers a promising solution for balancing data privacy and usability.
  • The method contributes to secure data sharing and processing in big data ecosystems.