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Mining Cloud 3D Video Data for Interactive Video Services

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Abstract

Cloud computing is an emerging technique. Here comes many new development directions. Many researchers over the world show great interests in cloud architecture. Cloud-based video solutions are proposed to meet many technical challenges in the real world. However, the data security is a vital problem, especially about video data. This paper proposed an architecture to mine the huge amount 3D video data. Meanwhile, we designed a model, called Key Encryption Model, to protect the privacy video data. In the meantime, the video mining cloud can decrypt the video data to make sure that the video data is safe and the mining results won’t be stolen by the third party. The result proves that transmitting the encrypted video is feasible. Besides, the outlook in this field is also pointed out.

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References

  1. Wen Y, Zhu X, Rodrigues J, Chen C (2014) Cloud mobile media: reflections and outlook. IEEE Trans Multimed 16(4):885–902

    Article  Google Scholar 

  2. Chen M, Mao S, Zhang Y, Leung V (2014) Big data: related technologies, challenges and future prospects, springerbriefs in computer science. Springer, ISBN 978-3-319-06245-7

  3. Gao P, Xiang W (2014) Rate-distortion optimized mode switching for error-resilient multi-view video plus depth based 3-D video coding. IEEE Trans Multimed 16(7):1797–1808

    Article  Google Scholar 

  4. Guan Z, Melodia T (2014) Cloud-assisted smart camera networks for energy-efficient 3D video streaming. IEEE Comput Soc 47(5):60–66

    Article  Google Scholar 

  5. Chen M (2014) NDNC-BAN: supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks. Inf Sci 284(10):142–156

    Article  Google Scholar 

  6. Zhang Y, Chen M, Mao S, Hu L, Leung V (2014) CAP: crowd activity prediction based on big data analysis. IEEE Netw 28(4):52–57

    Article  Google Scholar 

  7. Zhang Y, Zhang D, et al. (2014) CADRE: cloud-assisted drug recommendation service for online pharmacies, ACM/Springer mobile networks and applications. doi:10.1007/s11036-014-0537-4

  8. Lu W, Varna A, Wu M (2010) Security analysis for privacy preserving search of multimedia. In: Proceedings of 2010 IEEE 17th international conference on image processing, pp 2093–2096. doi:10.1109/ICIP.2010.5653399

  9. Tasevski P (2011) Password attacks and generation strategies. Tartu University, Faculty of Mathematics and Computer Sciences

  10. Yadav P, Rizvi S (2014) An exhaustive study on data mining techniques in mining of multimedia database. In: 2014 international conference on issues and challenges in intelligent computing techniques (ICICT), pp 541–545. doi:10.1109/ICICICT.2014.6781339

  11. Han J, Kamber M (2006) Data mining: concepts and techniques, morgan kaufmann publishers-an imprint of Elsevier, ISBN:0123814790 978012381479

  12. Xiang W, Gao P, Peng Q (2015) Robust multi-view 3D video communications based upon distributed video coding, to appear in IEEE Systems Journal

  13. Vijayakumar V, Nedunchezhian R (2012) A study on video data mining. Springer Int J Multimed Info Retr:153–172

  14. Bhatt C, Kankanhalli M (2011) Probabilistic Temporal Multimedia Data Mining. ACM Trans Intell Syst Technol 2(2):1–4

    Article  Google Scholar 

  15. Shen H, Li J (2009) A novel stream cipher for video compressed by H.264, Computer Society. doi:10.1109/IFCSTA.2009.134

  16. Massandy D, Munir I (2012) Secured video streaming development on smartphones with android platform. TSSA 16:339–344

    Google Scholar 

  17. Pudlewski S, Cen N, Guan Z Tommaso Melodia, video transmission over lossy wireless networks: a cross-layer perspective. IEEE J Selected Topics Signal Process 9(1):6–21

Download references

Acknowledgments

The work was supported in part by a Smart Futures Fellowship funded by the Queensland Government of Commonwealth Australia, and the Key Laboratory of Universal Wireless Communications (Beijing University of Posts and Telecommunications), Ministry of Education, P. R. China.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China

    Ting Xu, Qing Guo & Lifeng Mo

  2. School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia

    Wei Xiang

Authors
  1. Ting Xu
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  2. Wei Xiang
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  3. Qing Guo
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  4. Lifeng Mo
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Corresponding author

Correspondence to Qing Guo.

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Xu, T., Xiang, W., Guo, Q. et al. Mining Cloud 3D Video Data for Interactive Video Services. Mobile Netw Appl 20, 320–327 (2015). https://doi.org/10.1007/s11036-015-0596-1

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  • DOI: https://doi.org/10.1007/s11036-015-0596-1

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