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Partitioning-based mechanisms under personalized differential privacy.

Haoran Li1, Li Xiong1, Zhanglong Ji2

  • 1Emory University.

Advances in Knowledge Discovery and Data Mining : ... Pacific-Asia Conference, PAKDD ..., Proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining
|September 22, 2017
PubMed
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This study introduces personalized differential privacy using partitioning methods to balance individual privacy needs with data utility. It optimizes privacy budgets for better aggregate analysis results.

Area of Science:

  • Computer Science
  • Data Privacy
  • Statistical Analysis

Background:

  • Differential privacy offers strong data protection but applies a uniform privacy level to all users.
  • Individual data owners often have varying privacy preferences.
  • A single privacy parameter can lead to over-protection for some and under-protection for others.

Purpose of the Study:

  • To develop methods for personalized differential privacy.
  • To address the limitations of global differential privacy parameters.
  • To maximize data utility in private statistical analysis.

Main Methods:

  • Proposed two partitioning-based mechanisms: privacy-aware and utility-based partitioning.
  • Developed a t-round partitioning strategy to optimize privacy budget usage.

Related Experiment Videos

  • Implemented personalized differential privacy parameters for individuals.

    • Main Results:

    • Demonstrated effectiveness of partitioning mechanisms in handling personalized privacy needs.
    • Achieved a balance between individual privacy protection and aggregate data utility.
    • Showcased reduced privacy budget waste and enhanced utility through experiments.

    Conclusions:

    • Partitioning-based mechanisms effectively enable personalized differential privacy.
    • The proposed methods offer a flexible approach to managing privacy preferences in statistical analysis.
    • These techniques improve the practical application of differential privacy in real-world datasets.