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  3. Preserving Missing Data Distribution In Synthetic Data.
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Preserving Missing Data Distribution in Synthetic Data.

Xinyue Wang1, Hafiz Asif1, Jaideep Vaidya1

  • 1Rutgers University, Newark, USA.

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|January 28, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces novel methods for generating synthetic data that retain the informational value of missing data points. This approach enhances privacy-preserving data analysis by preserving crucial missing data distributions.

Keywords:
GANMissing DataPrivacySynthetic Data Generation

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Area of Science:

  • Computer Science
  • Data Science
  • Statistics

Background:

  • Web data is often sensitive and requires privacy-preserving methods for analysis.
  • Synthetic data generation is a key technique for protecting sensitive information.
  • Missing data in web artifacts contains valuable information often lost during traditional data preprocessing.

Purpose of the Study:

  • To develop and evaluate methods for generating synthetic data that preserve both observable and missing data distributions.
  • To address the loss of information inherent in imputation or deletion of missing data before synthetic data generation.

Main Methods:

  • Proposed novel methods for synthetic data generation.
  • Focused on preserving the distribution of both observed and missing data.
  • Conducted extensive empirical evaluations on fabricated and real-world datasets.

    • Main Results:

    • Demonstrated the effectiveness of the proposed methods in preserving missing data distributions.
    • Showcased the ability of synthetic data to retain informational content from missingness.
    • Empirical evaluations confirmed the utility of the approach across various datasets.

    Conclusions:

    • The proposed methods offer a significant advancement in privacy-preserving synthetic data generation.
    • Preserving missing data distributions is crucial for maintaining data utility in sensitive web data analysis.
    • This approach enables more robust and informative data analysis from web artifacts.