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Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data.

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New profiling models can re-identify individuals using behavioral data, even with privacy protections. This research highlights significant privacy risks in data sharing and challenges current deidentification standards for anonymous data.

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Area of Science:

  • Computer Science
  • Data Privacy
  • Behavioral Analytics

Background:

  • Daily technology interactions generate vast behavioral data, fueling scientific progress.
  • Sharing this data raises privacy concerns due to potential individual re-identification.
  • Existing re-identification methods often require substantial auxiliary information, limiting their effectiveness.

Purpose of the Study:

  • To develop and evaluate an entropy-based profiling model for learning time-persistent individual profiles.
  • To assess the model's accuracy in re-identifying individuals using limited auxiliary information.
  • To challenge current deidentification practices and legal definitions of anonymous data.

Main Methods:

  • Proposed an entropy-based profiling model to learn time-persistent behavioral profiles.
  • Utilized auxiliary information from a single target over a non-overlapping time period.
  • Tested the model on a large location dataset (0.5 million individuals) and a grocery shopping dataset (85,000 individuals).

Main Results:

  • Achieved 79% identification accuracy in a large location dataset and 65.2% in a grocery shopping dataset.
  • Demonstrated that identification accuracy decreases slowly over time.
  • Showed model robustness against state-of-the-art noise addition techniques.

Conclusions:

  • Individuals can be re-identified with significantly less auxiliary information than previously assumed.
  • Current deidentification practices may not adequately protect individual privacy.
  • The findings necessitate a re-evaluation of what constitutes legally anonymous data.