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Privacy-Preserving Two-Party Skyline Queries Over Horizontally Partitioned Data

Abstract : Skyline queries are an important type of multi-criteria analysis with diverse applications in practice (e.g., personalized services and intelligent transport systems). In this paper, we study how to answer skyline queries efficiently and in a privacy-preserving way when the data are sensitive and distributedly owned by multiple parties. We adopt the classical honest-but-curious attack model, and design a suite of efficient protocols for skyline queries over horizontally partitioned data. We analyze in detail the efficiency of each of the proposed protocols as well as their privacy guarantees.
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Ling Chen, Ting Yu, Rada Chirkova. Privacy-Preserving Two-Party Skyline Queries Over Horizontally Partitioned Data. 10th IFIP International Conference on Information Security Theory and Practice (WISTP), Sep 2016, Heraklion, Greece. pp.187-203, ⟨10.1007/978-3-319-45931-8_12⟩. ⟨hal-01639604⟩

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