Affiliations: [a] Department of Computer Science, North Carolina State University, NC, USA. E-mails: lchen10@ncsu.edu, rychirko@ncsu.edu | [b] Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar. E-mail: tyu@qf.org.qa
Note: [1] This paper is an extended version of our previous work published at DBSec 2017 [8]. Our previous work proposed a novel spatial decomposition technique k-skyband tree specially optimized for k-skyband queries, which partitions data adaptively based on the parameter k. In this work, we presented more experiments to demonstrate the reliability of the optimized algorithm and the superiority over baseline algorithm on fan-shaped random data inspired by realistic applications. We also presented the extension of the optimized algorithm to handle 3-dimension datasets and presented the effectiveness of the extension.
Abstract: Given a set of multi-dimensional points, a k-skyband query retrieves those points dominated by no more than k other points. k-skyband queries are an important type of multi-criteria analysis with diverse applications in practice. In this paper, we investigate techniques to answer k-skyband queries with differential privacy. We first propose a general technique BBS-Priv, which accepts any differentially private spatial decomposition tree as input and leverages data synthesis to answer k-skyband queries privately. We then show that, though quite a few private spatial decomposition trees are proposed in the literature, they are mainly designed to answer spatial range queries. Directly integrating them with BBS-Priv would introduce too much noise to generate useful k-skyband results. To address this problem, we propose a novel spatial decomposition technique k-skyband tree specially optimized for k-skyband queries, which partitions data adaptively based on the parameter k and performs finer partitions on the regions that are likely to contain k-skyband results. We further propose techniques to generate a k-skyband tree over spatial data that satisfies differential privacy, and combine BBS-Priv with the private k-skyband tree to answer k-skyband queries. We conduct extensive experiments based on two real-world datasets and three synthetic datasets that are commonly used for evaluating k-skyband queries. The results show that the proposed scheme significantly outperforms existing differentially private spatial decomposition schemes and achieves high utility when privacy budgets are properly allocated.
