Optimal univariate microaggregation with data suppression

Microaggregation is a disclosure limitation method that provides security through k-anonymity by modifying data before release but does not allow suppression of data. We define the microaggregation problem with suppression (MPS) to accommodate data suppression, and present a polynomial-time algorithm, based on dynamic programming, for optimal univariate microaggregation with suppression. Experimental results demonstrate the practical benefits of suppressing a few carefully selected data points during microaggregation using our method.

► We present a method for disclosure limitation through microaggregation with suppression.
► Privacy is preserved by modifying sensitive information prior to release.
► Our method reduces information loss due to record modification by suppressing up to p data points.
► Dynamic programming method provides a provably optimal solution.