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Computing Variance under Hierarchical Privacy-Related Interval Uncertainty

  • Hung T. Nguyen
  • Vladik Kreinovich
  • Berlin Wu
  • Gang Xiang
Part of the Studies in Computational Intelligence book series (SCI, volume 393)

Formulation and Analysis of the Problem and the Resulting Algorithms

Need for hierarchical statistical analysis. In the above text, we assumed that we have all the data in one large database, and we process this large statistical database to estimate the desired statistical characteristics.

To prevent privacy violations, we replace the original values of the quasiidentifier variables with ranges. For example, we divide the set of all possible ages into ranges [0, 10], [10, 20], [20, 30], etc. Then, instead of storing the actual age of 26, we only store the range [20, 30] which contains the actual age value.

Keywords

Interval Uncertainty Term Versus Computing Versus Maximum Versus Computing Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hung T. Nguyen
    • Vladik Kreinovich
      • Berlin Wu
        • Gang Xiang

          There are no affiliations available

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