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. 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.
Chen, L., Yu, T., & Chirkova, R. (2017). Differentially private k-skyband query answering through adaptive spatial decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10359 LNCS, pp. 142–163). Springer Verlag. https://doi.org/10.1007/978-3-319-61176-1_8