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Evaluation of Open-Source Tools for Differential Privacy.

Shiliang Zhang1,2, Anton Hagermalm1, Sanjin Slavnic1

  • 1Computer Science and Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden.

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|July 29, 2023
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Summary

This study introduces an evaluation framework for differential privacy (DP) tools, comparing their privacy-utility tradeoffs and resource usage. Results guide developers in selecting optimal DP solutions for diverse applications.

Keywords:
differential privacyevaluationopen-source tools

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Area of Science:

  • Computer Science
  • Data Privacy
  • Software Engineering

Background:

  • Differential privacy (DP) offers quantifiable privacy protection by adding noise to data, creating a privacy-utility tradeoff.
  • Numerous open-source tools exist for DP implementation, but a comprehensive comparison is lacking.
  • Evaluating these tools on privacy-utility balance and system resource usage is crucial for practical adoption.

Purpose of the Study:

  • To propose and utilize an open-source evaluation framework for comparing differential privacy tools.
  • To assess the performance of leading DP tools, including OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib.
  • To analyze the privacy-utility tradeoff and system resource consumption across different data sizes and attribute types (discrete/continuous).

Main Methods:

  • Development of an open-source evaluation framework for privacy protection solutions.
  • Empirical evaluation of five prominent open-source DP tools.
  • Quantification of performance metrics, including privacy-utility balance and system resource usage.
  • Testing across varying data sizes and for both discrete and continuous data attributes.

Main Results:

  • The study reveals distinct performance patterns among the evaluated DP tools.
  • Significant variations exist in how tools balance privacy protection against data utility.
  • Resource consumption (e.g., memory, computation time) differs notably across the surveyed tools.
  • Performance is influenced by data characteristics, such as size and attribute type (discrete vs. continuous).

Conclusions:

  • The findings provide practical insights for developers selecting DP tools based on specific application requirements.
  • This evaluation serves as a foundation for informed choices and faster adoption of differential privacy techniques.
  • Understanding tool-specific tradeoffs is essential for effective and efficient privacy-preserving data analysis.