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User abnormal behavior recommendation via multilayer network.

Chengyun Song1, Weiyi Liu2, Zhining Liu3

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Summary
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This study introduces a novel privacy-preserving recommendation system using graph analysis to detect abnormal user behavior online. The system achieves high precision and recall, protecting user data effectively.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Online services like banking and shopping necessitate privacy-preserving systems for detecting abnormal user behavior.
  • Machine learning models require extensive features, posing a challenge for privacy when using sensitive user data.

Purpose of the Study:

  • To develop a novel privacy-preserving recommendation system for identifying abnormal user behavior.
  • To address the dilemma between feature requirements for machine learning and data privacy concerns.

Main Methods:

  • Utilized graph analysis within a multilayer network framework.
  • Employed a large dataset with over 40,000 nodes and 43 million encrypted features.
  • Evaluated system performance on abnormal user behavior detection.

Main Results:

  • Achieved a precision rate of approximately 0.9, recall of 1.0, and F1-score of 0.94.
  • Demonstrated linear time complexity for the developed system.
  • Received up to 85% user satisfaction in real-world deployments.

Conclusions:

  • The proposed graph analysis approach effectively builds a privacy-preserving recommendation system for abnormal user behavior.
  • The system offers high accuracy and efficiency, suitable for practical applications.
  • User feedback confirms the system's effectiveness and satisfaction.