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A game theory-based block image compression method in encryption domain

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Abstract

With the development of digital imaging technology and the prevalence of mobile devices with camera, internet privacy has been a growing concern for public. Especially in some private chat groups in social network, for example, Facebook, WeChat, and MySpace, some people want to share their personal images by internet service provider without leaking this information while the internet service provider can also take some additional measures on these images, for example, reducing the network bandwidth. How to provide technologies or software with such functionalities, usually conflicting goals, becomes increasingly urgent in both academia and industry. Image encryption is a choice; however, it does not provide the additional function needed by internet service providers. Recently, game theory is widely used in network security to solve such problem with conflicting goals. In fact, there is a game theory between users and service providers. This paper proposes a block-based image compression method in encrypted domain which provides not only the privacy protection capability but also the additional operation capability needed by internet service providers. This block-based method can be formulated as a game theoretical problem and can be optimized by game theory. First, the image to be shared will be encrypted in block-by-block way by owner with simple encryption operation. Second, the service providers can send the part or full of the encrypted image stream according to the available bandwidth with an adaptive parameter sets. Finally, the intended receivers can decrypt the received encrypted stream to recover the image. Extensive experiments show that the proposed algorithm improves the compression performance compared with the-state-of algorithms.

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References

  1. http://weixin.qq.com/

  2. http://recode.net/2014/05/28/look-at-this-were-uploading-and-sharing-a-staggering-1-8-billion-photos-a-day/

  3. Molina B (2014) Personal Apple: iCloud not breached in celebrity photo leak. Sep. 2014. online available: http://www.usatoday.com/story/tech/personal/2014/09/02/apple-icloud-leak/14979323/

  4. Nie XS, Liu J, Sun JD, Wang LQ, Yang XH (2013) Robust video hashing based on representative-dispersive frames. Sci China Inform Sci 56(6):1–11

    Article  MathSciNet  Google Scholar 

  5. Squicciarini AC, Lin D, Sundareswaran S, Wede J (2014) Privacy policy inference of user-uploaded images on content sharing sites. IEEE Trans Knowl Data Eng. doi:10.1109/TKDE.2014.2320729

  6. Mitchell J (2010) Making photo tagging easier. Online Available: http://blog.facebook.com/blog.php?post=467145887130

  7. Gao W, Tian YH, Huang TJ, Yang Q (2010) Vlogging: a survey of videoblogging technology on the web. ACM Comput Surv 42(4): Article No.15

  8. Zhu, H, Dusit N, Walid S, Tamer B (2012) Game theory in wireless and communication networks-theory, models and applications. Cambridge Uinversity Press, Cambridge

  9. Roy S, Ellis C, Shiva S, Dasgupta D, Shandilya V, Wu QS (2010) A survey of game theory as applied to network security. In: Proceedings of the 43rd Hawaii international conference on system sciences, Honolulu, HI, pp 1–10

  10. Mohammad H, Zhu QY, Tansu A, Tamer B, Hubaux JP (2013) Game theory meets network security and privacy. ACM Comput Surv 45(3): Article 24

  11. Lye KW, Wing JM (2005) Game strategies in network security. Int J Inf Secur 4:71–86

    Article  Google Scholar 

  12. Cheng H, Li XB (2000) Partial encryption of compressed images and videos. IEEE Trans Signal Process 48(8):2439–2451

    Article  Google Scholar 

  13. Signal Guest editors: Piva A, Katzenbeisser S (2007) Processing in the encrypted domain. EURASIP J Inform Secur 2007

  14. Johnson M, Ishwar P, Prabhakaran VM, Schonberg D, Ramchandran K (2004) On compressing encrypted data. IEEE Trans Signal Process 52(10): 2992–3006

  15. Zhang GC, Liu SH, Jiang F, Zhao DB, Gao W (2013) An improved image compression scheme with an adaptive parameters set in encrypted domain. VCIP 2013:1–6

    Google Scholar 

  16. Hassinen M, Mussalo P (2005) Client controlled security for web applications. In: The 30th anniversary on local computer networks, IEEE Computer Society, pp 810–816

  17. Poller A, Steinebach M, Liu H (2012) Robust image obfuscation for privacy protection in Web 2.0 applications. IS&T SPIE Electronic Imaging. In: International society for optics and photonics, SPIE, 830304-830304-15

  18. Zhou Y, Panetta K, Agaian S (2009) Image encryption using discrete parametric cosine transform. In: Proceedings of the 43rd asilomar conference on signals, systems and computers, ser. Asilomar’09. Piscataway, NJ, USA. IEEE Press, pp 395–399

  19. Podesser M, Schmidt HP, Uhl A (2002) Selective bitplane encryption for secure transmission of image data in mobile environments. In: The 5th nordic signal processing symposium, pp 10–37

  20. Yekkala A, Madhavan CEV (2007) Bit plane encoding and encryption. In: Proceedings of the 2nd international conference on pattern recognition and machine intelligence, ser. PReMI’07. Springer, Berlin, pp 103–110

  21. Xiong ZX, Liveris AD, Cheng S (2004) Distributed source coding for sensor networks. IEEE Signal Process Mag 21(5):80–94

    Article  Google Scholar 

  22. Schonberg D, Draper SC, Ramchandran K (2005) On blind compression of encrypted correlated data approaching the source entropy rate. In: Proceedings of the 43rd Annual Allerton Conference, Allerton, IL

  23. Schonberg D, Draper SC, Yeo CH, Ramchandran K (2008) Toward compression of encrypted images and video sequences. IEEE Trans Inform Forensics Secur 3(4):749–762

    Article  Google Scholar 

  24. Lazzeretti R, Barni M (2008) Lossless compression of encrypted greylevel and color images. In: Proceedings of the 16th EUSIPCO, Lausanne, Switzerland

  25. Kumar AA, Makur A (2009) Lossy compression of encrypted image by compressive sensing technique. TENCON 2009–2009 IEEE Region 10 Conference, pp 1–5

  26. Liu W, Zeng W, Dong L, Yao Q (2010) Efficient compression of encrypted grayscale images. IEEE Trans Image Process 19(4):1097–1102

    Article  MathSciNet  Google Scholar 

  27. Kang XG, Xu XY, Peng AJ, Zeng WJ (2012) Scalable lossy compression for pixel-value encrypted images. In: IEEE data compression conference 2012, Snowbird, Utah, USA April 10–12

  28. Kang XG, Peng AJ, Xu XY, Cao XC (2013) Performing scalable lossy compression on pixel encrypted images. EURASIP J Image Video Process 32:1–6

  29. Zhang XP (2011) Lossy compression and iterative reconstruction for encrypted image. IEEE Trans Inform Forensics Secur 6(1):53–58

    Article  Google Scholar 

  30. Zhang J, Xiong RQ, Ma SW, Zhao DB (2011) High-quality image restoration from partial random samples in spatial domain. In: IEEE visual communications and image processing (VCIP), pp 1–4

  31. Ahmad I, Luo JC (2006) On using game theory to optimize the rate control in video coding. IEEE Trans CSVT 16(2):209–219

    Google Scholar 

  32. http://sipi.usc.edu/database/

  33. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  34. Lagendijk RL, Erkin Z, Barni M (2013) Encrypted signal processing for privacy protection: conveying the utility of homomorphic encryption and multiparty computation. IEEE Signal Process Mag 30(1):82–105

    Article  Google Scholar 

  35. Yao Q, Zeng W, Liu W (2009) Multi-resolution based hybrid spatiotemporal compression of encrypted videos. In: Proceedings of the IEEE international conference on acoustics speech and signal processing, Taipei, Taiwan, ROC, 725–728

Download references

Acknowledgments

This work was supported in part by the Major State Basic Research Development Program of China under Grant 2015CB351804, a scholarship from the China Scholarship Council of the Republic of China under Grant No. 201203070360 and the Natural Science Foundation of China under Grant No.60803147. The authors would like to thank the authors of  [29, 30] for kindly providing their source codes.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Shaohui Liu & Guochao Zhang

  2. School of Computer Science and Engineering, Kyungpook National University, Taegu, South Korea

    Anand Paul

  3. Department of Embedded Systems Engineering, College of Information and Technology, Incheon National University, Incheon, South Korea

    Gwanggil Jeon

Authors
  1. Shaohui Liu
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  2. Anand Paul
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  3. Guochao Zhang
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  4. Gwanggil Jeon
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Correspondence to Shaohui Liu.

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Liu, S., Paul, A., Zhang, G. et al. A game theory-based block image compression method in encryption domain. J Supercomput 71, 3353–3372 (2015). https://doi.org/10.1007/s11227-015-1413-0

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  • DOI: https://doi.org/10.1007/s11227-015-1413-0

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