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Computing over encrypted spatial data generated by IoT

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Abstract

Proliferation of IoT devices produces the enormous amount of data that need to be stored on clouds. A main focus of this paper is to ensure the secrecy of data, while it is in transit through unsecure communication media from edge devices to cloud and to provide security to the data once it is stored on public cloud. A new techniques based on computing over encrypted data i.e. homomorphic encryption seems to be promising methods. However, most of the previous works supporting computing over encrypted data are neither efficient nor compatible with IoT data and real time spatial data streams because there is a huge difference between normal data and spatial data. In this paper, we proposed a framework for computing over IoT generated encrypted spatial data. In order to provide computation over encrypted data first it needs to be indexed in standard data structure. For indexing encrypted data, we used R tree and its variants. We also proposed a method of most efficient and scalable, parallel construction of R trees and its variants on real time encrypted spatial data. We fired spatial range queries on encrypted spatial data. Specifically, the spatial range query execution time over encrypted spatial data of our proposed scheme is extremely efficient which takes slightly more time as taken by normal spatial range query executed over non-encrypted real time spatial data. Our scheme is not only efficient, but also highly compatible and scalable with IoT generated spatial data. Moreover, we rigorously define the scalability, query performance time, analyze the security of our schemes, and also conduct extensive experiments with a real time spatial dataset to demonstrate the performance of our schemes.

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Acknowledgements

The authors gratefully acknowledge the support provided by 5G and IoT Lab, DoECE, and TBIC-Shri Mata Vaishno Devi University, Katra, Jammu. The authors would also like to thank the anonymous reviewers for their constructive comments and suggestions to improve the quality of the paper.

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  1. School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, J&K, India

    Suresh V. Limkar & Rakesh Kumar Jha

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Correspondence to Rakesh Kumar Jha.

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Appendix

Appendix

After applying homomorphic encryption to the Table 8, resulting encrypted values are shown in below Table 16.

Table 16 Applying encryption to Table 8
Full size table

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Limkar, S.V., Jha, R.K. Computing over encrypted spatial data generated by IoT. Telecommun Syst 70, 193–229 (2019). https://doi.org/10.1007/s11235-018-0479-4

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