Efficient Sanitization of Unsafe Data Correlations

نویسندگان

  • Bechara al Bouna
  • Chris Clifton
  • Qutaibah M. Malluhi
چکیده

In this paper, we present a study to counter privacy violation due to unsafe data correlation. We propose a safe correlation requirement to keep correlated values bounded by l-diversity and evaluate the trade-off to be made for the sake of a strong privacy guarantee. Finally, we present a correlation sanitization algorithm that enforces our safety constraint and demonstrates its efficiency.

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تاریخ انتشار 2015