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Analysis, Design, and Implementation of a User-Friendly Differential Privacy Application.

Reynardo Tjhin1, Muhammad Sajjad Akbar1, Clement Canonne1

  • 1School of Computer Science, Faculty of Engineering, J12-Computer Science Building, University of Sydney, Sydney, NSW 2050, Australia.

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This study introduces a user-friendly web application for differential privacy (DP), making data privacy techniques more accessible. It demonstrates DP for queries and datasets, identifying implementation gaps in libraries.

Keywords:
AIanonymizationapplicationdifferential privacymachine learningprivacysecurity

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Area of Science:

  • Computer Science
  • Data Privacy
  • Artificial Intelligence

Background:

  • Protecting individual privacy in public data is crucial due to linkage attack risks.
  • Differential privacy (DP) offers robust privacy but is often inaccessible to non-experts due to its complexity.

Purpose of the Study:

  • To develop a user-friendly web application to bridge the gap between DP theory and practice.
  • To enhance public understanding and practical application of differential privacy mechanisms.

Main Methods:

  • Developed a web application with query and dataset privatization features.
  • Utilized OpenDP library for implementing differential privacy mechanisms.
  • Identified discrepancies in OpenDP library's maximum and minimum query implementations.

Main Results:

  • The application successfully demonstrates DP for statistical queries and dataset privatization.
  • Discrepancies between theoretical DP concepts and OpenDP library implementations were identified.
  • A foundational framework for dataset privatization using OpenDP was established.

Conclusions:

  • The developed web application enhances accessibility and understanding of differential privacy.
  • Identified implementation gaps in the OpenDP library highlight areas for improvement.
  • The tool serves as an educational resource and aids in addressing practical DP challenges.