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Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier.

Weiyi Xia1, Raymond Heatherly2, Xiaofeng Ding3

  • 1EECS Dept., Vanderbilt University, Nashville, TN, USA.

CODASPY : Proceedings of the ... ACM Conference on Data and Application Security and Privacy. ACM Conference on Data and Application Security & Privacy
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Summary

This study introduces a novel approach to de-identify person-specific data, creating an optimal trade-off between privacy risk and data utility. It demonstrates superior performance over existing methods for secure data sharing.

Keywords:
De-identificationExperimentationManagementOptimizationPolicyPrivacySecurity

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Area of Science:

  • Computer Science
  • Information Science
  • Data Privacy

Background:

  • Modern IT systems generate vast amounts of person-specific data.
  • Organizations need to share this data for secondary uses while ensuring de-identification.
  • Previous de-identification policy searches were limited by syntactic utility and fixed risk thresholds.

Purpose of the Study:

  • To develop an optimal set of de-identification policies balancing privacy risk (R) and utility (U), forming a R-U frontier.
  • To introduce a semantic definition of utility compatible with lattice-based policy representation.
  • To improve upon existing methods for searching de-identification policies.

Main Methods:

  • Introduced a semantic, information-theoretic definition of data utility.
  • Developed a lattice-based model for de-identification policies.
  • Employed a probability-guided heuristic search to identify policies on the R-U frontier.
  • Validated the approach using the Adult dataset.

Main Results:

  • Constructed a R-U frontier, representing optimal trade-offs between privacy risk and data utility.
  • The heuristic search identified superior policies by examining fewer options compared to competitive approaches.
  • Demonstrated that the Safe Harbor standard is suboptimal compared to the discovered frontier.

Conclusions:

  • The proposed method effectively constructs an optimal R-U frontier for de-identification policies.
  • This approach offers a more effective way to balance data privacy and utility for secondary data sharing.
  • The findings suggest re-evaluation of current de-identification standards like Safe Harbor.