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Authentication of outsourced linear function query with efficient updates

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Abstract

Storing the large-scale data on the cloud server side becomes nowadays an alternative for the data owner with the popularity and maturity of the cloud computing technique, where the data owner can manage the data with limited resources, and the user issues the query request to the cloud server instead of the data owner. As the server is not completely trusted, it is necessary for the user to perform results authentication to check whether or not the returned results from the cloud server are correct. We investigate in this paper how to perform efficient data update for the result authentication of the outsourced univariate linear function query. We seek to outsource almost all the data and computing to the server, and as few data and computations as possible are stored and performed on the data owner side, respectively. We present a novel scheme to achieve the security goal, which is divided into two parts. The first part is a verification algorithm for the outsourced computing of line intersections, which enables the data owner to store most of the data on the server side, and to execute less of the computing of the line intersections. The second part is an authentication data structure Two Level Merkle B Tree for the outsourced univariate linear function query, where the top level is used to index the user input and authenticate the query results, and the bottom level is used to index the query condition and authenticate the query results. The authentication data structure enables the data owner to update the data efficiently, and to implement the query on the server side. The theoretic analysis shows that our proposed scheme works with higher efficiency.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 11271003, Guangdong Province Natural Science Foundation of major basic research and Cultivation Project under Grant No. 2015A030308016, Basic Research Major Projects of Department of Education Guangdong Province under Grant No. 2014KZDXM044, Innovation Team Construction Project of Guangdong Province Regular Universities under Grant No. 2015KCXTD014, Scientific Research Fund of Hunan Provincial Education Department under Grant No. 15C0536, and Collaborative Innovation Major Projects of Bureau of Education of Guangzhou City under Grant No. 1201610005.

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Authors and Affiliations

  1. College of Mathematics and Information Science, Guangzhou University, Guangzhou, China

    Gang Sheng & Chunming Tang

  2. School of Mathematics Science, Liaocheng University, Liaocheng, China

    Hongyan Han

  3. School of Mathematics and Statistics Science, Ludong University, Yantai, China

    Wei Gao

  4. School of Mathematics and Computational Science, Hunan University of Science and Technology, Xiangtan, China

    Xing Hu

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  1. Gang Sheng
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  2. Chunming Tang
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  3. Hongyan Han
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  4. Wei Gao
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  5. Xing Hu
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Correspondence to Chunming Tang.

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Sheng, G., Tang, C., Han, H. et al. Authentication of outsourced linear function query with efficient updates. Cluster Comput 22 (Suppl 4), 10031–10039 (2019). https://doi.org/10.1007/s10586-017-1064-4

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