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Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach.

Haoran Li1, Xiaoqian Jiang2, Li Xiong1

  • 1Emory University, Atlanta, GA.

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
|March 15, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods for real-time, differentially private data release. These techniques improve data utility for dynamic datasets compared to existing approaches.

Keywords:
Differential privacyadaptive samplingdynamic dataset release

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Area of Science:

  • Computer Science
  • Data Privacy
  • Statistical Analysis

Background:

  • Differential privacy is crucial for private statistical data release.
  • Existing methods primarily support one-time release of static datasets.
  • Real-time release of dynamic datasets presents challenges due to accumulated error and data correlations.

Purpose of the Study:

  • To develop methods for real-time, differentially private release of dynamic datasets.
  • To address the limitations of existing algorithms in handling sequential data releases.

Main Methods:

  • Introduced a novel adaptive distance-based sampling approach.
  • Developed DSFT (Differential Sampling with Fixed Threshold) for histogram release based on data change.
  • Developed DSAT (Differential Sampling with Adaptive Threshold) using dynamic threshold adjustment for improved accuracy.

Main Results:

  • Both DSFT and DSAT methods were evaluated on real and synthetic datasets.
  • Experimental results demonstrate superior utility compared to baseline and state-of-the-art methods.
  • The adaptive threshold in DSAT effectively captures data dynamics for better utility.

Conclusions:

  • The proposed adaptive distance-based sampling methods are effective for real-time differentially private data release.
  • These methods offer improved data utility for dynamic datasets.
  • The findings advance the field of private data release for time-series data.