Hierarchical Pareto Curve model for privacy skyline

Privacy is an essential issue in database publishing. Since the introduction of skyline operator in 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. Skyline Check 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 private skyline processing. In HPC, answers to the skyline query are interpolated by spline function and represented by a set of polynomial Pareto curves. Hence, skyline querying requests can be satisfied without disclosing the actual data points. Moreover, the accuracy of a skyline query can be controlled by setting the order of the polynomial expression and total number of Pareto curves. The HPC model can be extended for distributed and cooperative computing environments. With privacy embedded in piecewise Pareto curves and merging operators developed, distributed skyline processing becomes practical. From our preliminary experiments, the results show supportive indications towards the HPC model.