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• [P1032] Hierarchical Pareto Curve (HPC) Model for Privacy Skyline
  Author(s): Boris Y. Chan, Vincent Ng, Jacob Sun
  
  

  Abstract
  Privacy is an essential issue in database publishing. Since the introduction of skyline operator into database community, there was a few researches working on the privacy skyline and related the privacy theory, framework and model in last few years. For those algorithms (e.g. SkylineCheck and Privacy Diagnostics), centralized database is assumed and the consideration of concurrency and parallelism is in lack. In this paper, we propose the Hierarchical Pareto Curve (HPC) model for privacy skyline processing. In HPC model, answer to the skyline query is interpolated by Spline function and represented by a set of Pareto curves (also known as piecewise polynomial). Hence, skyline query is answered by Pareto curves with actual skyline embedded (without disclosing the actual data point). Moreover, the accuracy (mean square error) of the Pareto curves can be controlled by the order of the polynomial expression and total number of Pareto curves. We report the full descriptions of the definition and operation of HPC model in distributed and cooperative computing environments. In the preliminary experiments, the results show supportive indications towards the theory proposed.

  
   

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