Skip to main content
Springer Nature Link
Log in

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

  • Published:
Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ateniese G, Burns R, Curtmola R, Herring J, Kissner L, Peterson Z, Song D (2007) Provable data possession at untrusted stores. In: CCS 07, pp 598–609

  2. Boneh D, Freeman DM (2011) Homomorphic signatures for polynomial functions. In: Advances in cryptology-EUROCRYPT. Springer, pp 149–168

  3. Catalano D, Fiore D (2013) Practical homomorphic macs forarithmetic circuits. In: Advances in cryptology-EUROCRYPT. Springer, pp 336–352

  4. Cristina D, Elena A, Catalin L, Valentin C (2014) A solution for the management of multimedia sessions in hybrid clouds. Int J Space-Based Situat Comput 4(2):77–87

    Article  Google Scholar 

  5. Choi SG, Katz J, Kumaresan R, Cid C (2013) Multi-client non-interactive verifiable computation. In: Theory of cryptography. Springer, pp 499–518

  6. Chung KM, Kalai Y, Vadhan S (2010) Improved delegation of computation using fully homomorphic encryption. In: Advances in cryptology-CRYPTO. Springer, pp 483–501

  7. Fiore D, Gennaro R (2012) Publicly verifiable delegation of large polynomials and matrix computations, with applications. In: ACM conference on computer and communications security. ACM, pp 501–512

  8. Gennaro R, Gentry C, Parno B (2010) Non-interactive verifiable computing: outsourcing computation to untrusted workers. In: Advances in cryptology-CRYPTO. Springer, pp 465–482

  9. Gordon SD, Katz J, Liu F-H, Shi E, Zhou H-S (2015) Multi-client verifiable computation with stronger security guarantees. In: Theory of cryptography. Springer, pp 144–166

  10. Guo S, Xu H (2015) A secure delegation scheme of large polynomial computation in multi-party cloud. Int J Grid Util Comput 6(2):1–7

    Google Scholar 

  11. He D, Kumar N, Zeadally S, Wang H (2018) Certificateless provable data possession scheme for cloud-based smart grid data management systems. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2017.2761806

    Google Scholar 

  12. He D, Zeadally S, Wu L (2015) Certificateless public auditing scheme for cloud-assisted wireless body area networks. IEEE Syst J. https://doi.org/10.1109/JSYST.2015.2428620

    Google Scholar 

  13. jPBC: java pairing based cryptography. In: Proceeding of the 16th IEEE symposium on computers and communications, ISCC 2011, pp 850–855. http://gas.dia.unisa.it/projects/jpbc/

  14. Liu X, Zhang Y, Wang B, Yan J (2013) Mona: secure multiowner data sharing for dynamic groups in the cloud. IEEE Trans Parallel Distrib Syst 24(6):1182–1191

    Article  Google Scholar 

  15. Liu X, Sun W, Quan H, Lou W, Zhang Y, Li H (2017) Publicly verifiable inner product evaluation over outsourced data streams under multiple keys. IEEE Trans Serv Comput 10(5):826–838. https://doi.org/10.1109/TSC.2016.2531665

    Article  Google Scholar 

  16. Liu X, Deng R, Choo K, Yang Y, Pang HH (2018) Privacy-preserving outsourced calculation toolkit in the Cloud. IEEE Trans Dependable Secure Comput. https://doi.org/10.1109/TDSC.2018.2816656

    Google Scholar 

  17. Liu X, Lu R, Ma J, Chen L, Chen B (2016) Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J Biomed Health Inf 20(2):655–668

    Article  Google Scholar 

  18. Liu X, Choo R, Deng R, Lu R, Weng J (2018) Efficient and privacy-preserving outsourced computation of rational numbers. IEEE Trans Dependable Secure Comput 15(1):27–39. https://doi.org/10.1109/TDSC.2016.2536601

    Article  Google Scholar 

  19. Meriem T, Mahmoud B, Fabrice K (2014) An approach for developing an interoperability mechanism between cloud providers. Int J Space-Based Situat Comput 4(2):88–99

    Article  Google Scholar 

  20. Nath S, Venkatesan R (2013) Publicly verifiable grouped aggregation queries on outsourced data streams. In: International conference on data engineering. IEEE, pp 517–528

  21. Papadopoulos S, Cormode G, Deligiannakis A, Garofalakis M (2013) Lightweight authentication of linear algebraic querieson data streams. In: International conference on management of data. ACM, pp 881–892

  22. Parno B, Raykova M, Vaikuntanathan V (2012) How to delegate and verify in public: verifiable computation from attribute-based encryption. In: Theory of cryptography. Springer, pp 422–439

  23. Shacham H, Waters B (2008) Compact proofs of retrievability. In: Pieprzyk J (ed) ASIACRYPT 2008, vol 5350. Springer, Heidelberg, pp 90–107 LNCS

    Chapter  Google Scholar 

  24. Sun W, Liu X, Lou W, Hou YT, Li H (2015) Catch you if you lie to me: efficient verifiable conjunctive keyword search over large dynamic encrypted cloud data. In: 2015 IEEE conference on computer communications (INFOCOM). IEEE, pp 2110–2118

  25. Tian H, Chen Y, Chang C, Jiang H, Huang Y, Chen Y, Liu J (2015) Dynamic-hash-table based public auditing for secure cloud storage. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2015.2512589

    Google Scholar 

  26. Wang Y, Du J, Cheng X, Liu Z, Lin K (2016) Degradation and encryption for outsourced PNG images in cloud storage. Int J Grid Util Comput 7(1):22–28

    Article  Google Scholar 

  27. Yu Y, Zhang Y, Ni J, Au M, Chen L, Liu H (2014) Remote data possession checking with enhanced security for cloud storage. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2014.10.006

    Google Scholar 

  28. Yu Y, Au MH, Ateniese G, Huang X, Susilo W, Dai Y, Min G (2017) Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Trans Inf Forens Secur. https://doi.org/10.1109/TIFS.2016.2615853

    Google Scholar 

  29. Yu Y, Li Y, Ni J, Yang G, Mu Y, Susilo W (2016) Comments on “public integrity auditing for dynamic data sharing with multi-user modification”. IEEE Trans Inf Forens Secur 11(3):658–659

    Article  Google Scholar 

  30. Yang Y, Zheng X, Liu X, Zhong S, Chang V (2017) Cross-domain dynamic anonymous authenticated group key management with symptom-matching for E-health social system. Future Gener Comput Syst 84:160–176

    Article  Google Scholar 

  31. Zhu S, Yang X (2015) Protecting data in cloud environment with attribute-based encryption. Int J Grid Util Comput 6(2):91–97

    Article  Google Scholar 

Download references

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).

Author information

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

Authors
  1. Xu An Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yudong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Arun Kumar Sangaiah
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jindan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xu An Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-018-0641-6

Keywords

Mathematics Subject Classification