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Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data.

Stefano Bennati1, Aleksandra Kovacevic2

  • 1HERE Technologies, Privacy Office, Zürich, Switzerland. stefano.bennati@here.com.

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|January 8, 2022
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Summary
This summary is machine-generated.

This study introduces a new model for assessing privacy risks in trajectory data, considering adversaries with imperfect knowledge. This approach offers a more accurate balance between data utility and individual privacy for location-based services.

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Area of Science:

  • Computer Science
  • Data Privacy
  • Location-Based Services

Background:

  • Trajectory data is valuable for location-based services but raises privacy concerns.
  • Existing privacy risk assessments often assume perfect adversary knowledge, overestimating threats.
  • Balancing data utility and privacy requires accurate measurement of both.

Purpose of the Study:

  • To develop a more realistic model for adversary knowledge in privacy risk assessment.
  • To introduce equivalence areas as a measure of adversary skill.
  • To derive privacy metrics from this new model for trajectory data.

Main Methods:

  • Introduced a model of an adversary with imperfect knowledge.
  • Defined equivalence areas as spatio-temporal regions with semantic meaning.
  • Derived standard privacy metrics (k-anonymity, l-diversity, t-closeness) from equivalence areas.

Main Results:

  • The proposed model provides a more accurate estimation of privacy risks.
  • Equivalence areas quantify adversary skill based on region size and accuracy.
  • Privacy metrics derived from equivalence areas are applicable to any dataset, regardless of anonymization.

Conclusions:

  • The equivalence area model offers a refined approach to managing privacy risks in trajectory data.
  • This method enables service providers to better optimize the privacy-utility trade-off.
  • Accurate privacy measurement is crucial for responsible data processing in location-based services.