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Information Architecture for Data Disclosure.

Kurt A Pflughoeft1, Ehsan S Soofi2, Refik Soyer3

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Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces an information theoretic framework for secure data disclosure, balancing intruder misuse prevention with legitimate user analysis needs. The maximum entropy model ensures data utility while preserving individual confidentiality.

Keywords:
Kullback–Leibler informationdata confidentialitydata utilitydifferential privacydisclosure riskmaximum entropy

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Area of Science:

  • Information Theory
  • Data Privacy
  • Statistical Modeling

Background:

  • Data disclosure is critical for organizations but poses significant confidentiality risks.
  • Balancing data utility for analysis with protection against misuse is a key challenge.
  • Existing methods may not adequately address both privacy and analytical needs simultaneously.

Purpose of the Study:

  • To propose a novel information theoretic architecture for the data disclosure problem.
  • To develop a framework that ensures data confidentiality while maintaining analytical utility.
  • To provide a method applicable to both univariate and multivariate data.

Main Methods:

  • Development of a maximum entropy (ME) model using statistical information from actual data.
  • Rigorous testing of the ME model's adequacy and performance.
  • Generation of disclosure data from the ME model and quantification of data discrepancy.

Main Results:

  • The proposed architecture effectively balances data utility and individual confidentiality.
  • The maximum entropy model provides a robust foundation for secure data release.
  • Quantifiable discrepancy metrics allow for controlled data disclosure.

Conclusions:

  • The information theoretic architecture offers a principled approach to data disclosure.
  • The maximum entropy framework is adaptable for diverse data types, including financial data.
  • This method enhances data security without compromising essential analytical insights.