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Improved publicly verifiable group sum evaluation over outsourced data streams in IoT setting

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Abstract

With the continuous development of the internet of things (IoT) technology, large amount of data has been generated by lots of IoT devices which require large-scale data processing technologies and storage technologies. Cloud computation is a paradigm for handling such massive data. With the help of cloud computing, IoT devices can utilize the data more efficiently, conveniently and faster. Therefore, how to promote the better integration of the IoT and cloud computing is an interesting research problem. In the big data era, group sum evaluation over outsourced data stream collected by IoT devices is an essential building block in many stream applications, such as statistical monitoring, data mining, machine learning and so on. Thus it is very valuable to design a mechanism to verify the correctness of the group sum evaluation over the outsourced data streams, especially when the data streams are originated from multiple data sources. Recently, Liu et al. proposed such a scheme to solve this problem. However in this paper, we show their scheme is not secure. Concretely, the adversary can easily forge tags for outsourced data, thus the correctness of the group sum evaluation can not be guaranteed anymore. Furthermore, we give two improved schemes which can resist our attack and analyze their security. Finally, we roughly evaluate the performance of our two improved schemes. Our first scheme almost shares the same efficiency as Liu et al.’s proposal but with no security flaw, the second scheme shares the same structure with Liu et al.’s proposal and can be compatible with the existing composite order bilinear pairing cryptosystem.

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Acknowledgements

This work is supported by National Cryptography Development Fund of China Under Grants No. MMJJ20170112, National Natural Science Foundation of China (Grant Nos. 61772550, 61572521, U1636114, 61402531), National Key Research and Development Program of China Under Grants No. 2017YFB0802000, Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2018JM6028 and 2016JQ6037) and Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201610).

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Authors and Affiliations

  1. Key Laboratory of Information and Network Security, Engineering University of Chinese Armed Police Force, Xi’an, People’s Republic of China

    Xu An Wang & Yudong Liu

  2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, People’s Republic of China

    Xu An Wang & Jindan Zhang

  3. School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

  4. Xianyang Vocational Technical College, Xianyang, People’s Republic of China

    Jindan Zhang

  5. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, People’s Republic of China

    Jindan Zhang

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  1. Xu An Wang
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  2. Yudong Liu
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  3. Arun Kumar Sangaiah
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Correspondence to Xu An Wang.

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Wang, X.A., Liu, Y., Sangaiah, A.K. et al. Improved publicly verifiable group sum evaluation over outsourced data streams in IoT setting. Computing 101, 773–790 (2019). https://doi.org/10.1007/s00607-018-0641-6

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