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A resource-aware approach for authenticating privacy preserving GNN queries

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Abstract

Nowadays many location service providers (LSPs) employ spatial databases outsourced from a third-party data owner (DO) to answer various users’ queries, e.g., group nearest neighbor (GNN) queries that enable a group of users to find a meeting place minimizing their aggregate travel distance. Along with the benefits from LSPs and DO, protection of location privacy and authentication of query results become two major concerns for users while assessing GNN queries. This paper proposes a resource-aware approach that supports effective location privacy preservation and efficient query result authentication with a less storage, communication and computation overhead. Specifically, two centroid-based techniques are investigated to generate a centroid point, which initiates GNN query on behalf of the group members. Then, an authentication algorithm based on Voronoi diagram is proposed for spatial queries. Finally, we demonstrate how our approach is resistant to various attacks, and evaluate its performance by comparing with three competitive approaches. The results show the proposed approach is better and more economical in terms of resource overhead, while considering both privacy preservation and query authentication.

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  1. http://chorochronos.datastories.org/?q=node/58

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (grants No. 61672133, No. 61632007 and No. 61602087), and the Fundamental Research Funds for the Central Universities (grants No. ZYGX2015J058 and No. ZYGX2014Z007).

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Authors and Affiliations

  1. Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Yan Dai, Jie Shao & Gang Hu

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Long Guo

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  1. Yan Dai
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  2. Jie Shao
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  3. Gang Hu
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  4. Long Guo
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Corresponding author

Correspondence to Jie Shao.

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This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications.

Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang

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Dai, Y., Shao, J., Hu, G. et al. A resource-aware approach for authenticating privacy preserving GNN queries. World Wide Web 22, 437–454 (2019). https://doi.org/10.1007/s11280-017-0507-4

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  • DOI: https://doi.org/10.1007/s11280-017-0507-4

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