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Fast and secured cloud assisted recovery scheme for compressively sensed signals using new chaotic system

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Abstract

With recent advancement in Sensors technology, multimedia data has been exponentially generated every day. As a result, there is always a huge demand for fast data processing and storage. In order to effectively acquire and process such a huge amount of data, the concept of compressive sensing (CS) as well as the abundant computing and storage resources of cloud have been increasingly used nowadays. In this paper, we propose a novel secured cloud assisted recovery scheme for compressively sensed signals using a proposed new chaotic map that has wider chaotic range and better attributes when compared to existing maps. With pseudo randomness and unpredictability of the chaotic sequence, generated using the proposed chaotic map, sensing matrix for CS problem and the encryption algorithm are designed. The proposed system ensures that the data owners securely outsource the compressively sensed samples to cloud which occupies less storage. Data users then insist cloud to perform the complex reconstruction problem in an encrypted domain with substantial computational cost being shifted to cloud. Cloud performs the expensive reconstruction problem and provides the reconstructed signal in encrypted form which is later decrypted by the data users. Cloud thus gets no knowledge about the original underlying data samples ensuring privacy of the proposed system. Empirical analysis on the proposed system shows satisfactory compression and security performance on both one dimensional and two dimensional data. The simulation results prove the efficiency of the proposed cloud assisted scheme.

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References

  1. <http://sipi.usc.edu/database/database.php?volume=misc>, accessed on April 2017

  2. Amiribesheli M, Asma B, Abdelhamid B (2015) A review of smart homes in healthcare. J Ambient Intell Humaniz Comput 6(4):495–517

    Article  Google Scholar 

  3. Ashwini K, Amutha R (2018) Compressive sensing based simultaneous fusion and compression of multi-focus images using learned dictionary. Multimed Tools Appl 1–16. https://doi.org/10.1007/s11042-018-5824-9

    Article  Google Scholar 

  4. Belazi A, El-Latif AAA, Belghith S (2016) A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process 128:155–170

    Article  Google Scholar 

  5. Candes EJ (2006) Compressive sampling. in: Proceedings of the international congress of mathematicians, vol. 3, Madrid, Spain, p 1433–1452

  6. Candes EJ (2008) The restricted isometry property and its implications for compressed sensing. C R Math 346(9–10):589–592

    Article  MathSciNet  Google Scholar 

  7. Candes E, Romberg J (2007) Sparsity and incoherence in compressive sampling. Inverse Probl 23(3):969

    Article  MathSciNet  Google Scholar 

  8. Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE T Inform Theory 52(12):5406–5425

    Article  MathSciNet  Google Scholar 

  9. Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Proc Mag 25(2):21–30

    Article  Google Scholar 

  10. Chen F, Xiang T, Yang Y, Chow SS (2016) Secure cloud storage meets with secure network coding. IEEE T Comput 65(6):1936–1948

    Article  MathSciNet  Google Scholar 

  11. Chen X, Huang X, Li J, Ma J, Lou W, Wong DS (2015) New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE T Inf Foren Sec 10(1):69–78

    Article  Google Scholar 

  12. Devaraj P, Kavitha C (2017) Crypt analysis of an image compression-encryption algorithm and a modified scheme using compressive sensing. Optik-International Journal for Light and Electron Optics

  13. Do TT, Gan L, Nguyen NH, Tran TD (2012) Fast and efficient compressive sensing using structurally random matrices. IEEE T Signal Proces 60(1):139–154

    Article  MathSciNet  Google Scholar 

  14. Donoho DL (2006) Compressed sensing. IEEE T Inform Theory 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  15. Dreier J, Kerschbaum F (2011) Practical privacy-preserving multiparty linear programming based on problem transformation. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, p 916–924, IEEE

  16. Fan F (2014) Toeplitz-structured measurement matrix construction for chaotic compressive sensing. In Intelligent Control and Information Processing (ICICIP), 2014 Fifth International Conference on, p 19–22, IEEE

  17. George SN, Pattathil DP (2014) A secure LFSR based random measurement matrix for compressive sensing. Sensing Imaging 15(1):85

    Article  Google Scholar 

  18. Gibson RM, Amira A, Ramzan N, Casaseca-de-la-Higuera P, Pervez Z (2017) Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomed Signal Proces 33:96–108

    Article  Google Scholar 

  19. Hu G, Xiao D, Wang Y, Xiang T (2017) An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image R 44:116–127

    Article  Google Scholar 

  20. Hu G, Xiao D, Xiang T, Bai S, Zhang Y (2017) A compressive sensing based privacy preserving outsourcing of image storage and identity authentication service in cloud. Inf Sci 387:132–145

    Article  Google Scholar 

  21. Hua Z, Zhou Y, Pun CM, Chen CP (2014) Image encryption using 2D Logistic-Sine chaotic map. In Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, p 3229–3234, IEEE

  22. Huang Z, Liu S, Mao X, Chen K, Li J (2017) Insight of the protection for data security under selective opening attacks. Inf Sci 412:223–241

    Article  Google Scholar 

  23. Lei X, Liao X, Huang T, Li H, Hu C (2013) Outsourcing large matrix inversion computation to a public cloud. IEEE Trans Cloud Comput 1(1):1

    Article  Google Scholar 

  24. Li J, Huang X, Li J, Chen X, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE T Parall Distr 25(8):2201–2210

    Article  Google Scholar 

  25. Li J, Li J, Chen X, Jia C, Lou W (2015) Identity-based encryption with outsourced revocation in cloud computing. IEEE T comput 64(2):425–437

    Article  MathSciNet  Google Scholar 

  26. Li J, Li YK, Chen X, Lee PP, Lou W (2015) A hybrid cloud approach for secure authorized deduplication. IEEE T Parall Distr 26(5):1206–1216

    Article  Google Scholar 

  27. Li P, Li J, Huang Z, Li T, Gao CZ, Yiu SM, Chen K (2017) Multi-key privacy-preserving deep learning in cloud computing. Future Gener Comp SY 74:76–85

    Article  Google Scholar 

  28. Lu X, Zheng X, Yuan Y (2017) Remote Sensing Scene Classification by Unsupervised Representation Learning. IEEE T Geosci Remote

  29. Nazare AC Jr, Schwartz WR (2016) A scalable and flexible framework for smart video surveillance. Comput Vis Image Und 144:258–275

    Article  Google Scholar 

  30. Phamila AV, Amutha R (2013) Low complexity energy efficient very low bit-rate image compression scheme for wireless sensor network. Inf Process Lett 113(18):672–676

    Article  MathSciNet  Google Scholar 

  31. Phamila AV, Amutha R (2013) Low complex energy aware image communication in visual sensor networks. J Electron Imaging 22(4):041107–041107

    Article  Google Scholar 

  32. Phamila AV, Amutha R (2015) Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks. Electron Lett 51(11):824–826

    Article  Google Scholar 

  33. Pincus S (1995) Approximate entropy (ApEn) as a complexity measure. Chaos: J Nonlinear Sci 5(1):110–117

    Article  MathSciNet  Google Scholar 

  34. Rashid F, Miri A (2016) Secure image data deduplication through compressive sensing. In Privacy, Security and Trust (PST), 2016 14th Annual Conference on, p. 569–572, IEEE

  35. Singh A, Chatterjee K (2017) Cloud security issues and challenges: a survey. J Netw Comput Appl 79:88–115

    Article  Google Scholar 

  36. Tatlas NA, Potirakis SM, Mitilineos SA, Rangoussi M (2015) On the effect of compression on the complexity characteristics of wireless acoustic sensor network signals. Signal Process 107:153–163

    Article  Google Scholar 

  37. Verma, P., Sood, S. K., & Kalra, S. (2017) Cloud-centric IoT based student healthcare monitoring framework. J Amb Intel Hum Comp, 1–17

  38. Wang C, Zhang B, Ren K, Roveda JM (2013) Privacy-assured outsourcing of image reconstruction service in cloud. IEEE Trans Emerg Topics Comput 1(1):166–177

    Article  Google Scholar 

  39. Wang C, Zhang B, Ren K, Roveda JM, Chen CW, Xu Z (2014) A privacy-aware cloud-assisted healthcare monitoring system via compressive sensing. In INFOCOM, 2014 Proceedings IEEE, p 2130–2138

  40. Yang Z, Zhou Q, Lei L, Zheng K, Xiang W (2016) An IoT-cloud based wearable ECG monitoring system for smart healthcare. J Med Syst 40(12):286

    Article  Google Scholar 

  41. Yao S, Wang T, Shen W, Shaoming P, Chong Y (2017) Research of incoherence rotated chaotic measurement matrix in compressed sensing. Multimed Tools Appl 76(17):17699–17717

    Article  Google Scholar 

  42. Yaseen Q, Aldwairi M, Jararweh Y, Al-Ayyoub M, Gupta B (2017) Collusion attacks mitigation in internet of things: a fog based model. Multimed Tools Appl, 1–20. doi: https://doi.org/10.1007/s11042-017-5288-3

    Article  Google Scholar 

  43. Yu C, Li J, Li X, Ren X, Gupta BB (2018) Four-image encryption scheme based on quaternion Fresnel transform, chaos and computer generated hologram. Multimed Tools Appl 77(4):4585–4608

    Article  Google Scholar 

  44. Yu L, Barbot JP, Zheng G, Sun H (2010) Compressive sensing with chaotic sequence. IEEE Signal Proc Let 17(8):731–734

    Article  Google Scholar 

  45. Yuan X, Wang X, Wang C, Weng J, Ren K (2016) Enabling secure and fast indexing for privacy-assured healthcare monitoring via compressive sensing. IEEE T Multimedia 18(10):2002–2014

    Article  Google Scholar 

  46. Zhang F, Ma X, Liu S (2014) Efficient computation outsourcing for inverting a class of homomorphic functions. Inf Sci 286:19–28

    Article  MathSciNet  Google Scholar 

  47. Zhang Y, Zhou J, Xiang Y, Zhang LY, Chen F, Pang S, Liao X (2017) Computation Outsourcing Meets Lossy Channel: Secure Sparse Robustness Decoding Service in Multi-Clouds. IEEE Transactions on Big Data

  48. Zhou N, Pan S, Cheng S, Zhou Z (2016) Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133

    Article  Google Scholar 

  49. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing. Optik-Int J Light Electron Optics 125(18):5075–5080

    Article  Google Scholar 

  50. Zhou N, Zhang A, Zheng F, Gong L (2014) Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Opt Laser Technol 62:152–160

    Article  Google Scholar 

  51. Zhu C, Hu Y, Zhou X (2014) A novel image encryption scheme based on the LSM chaotic system. Int J Sec Its Appl 8(6):61–70

    Google Scholar 

  52. Ziran P, Guojun W, Jiang H, Shuangwu M (2017) Research and improvement of ECG compression algorithm based on EZW. Comput Methods Prog Biomed 145:157–166

    Article  Google Scholar 

Download references

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Ashwini, K., Amutha, R. Fast and secured cloud assisted recovery scheme for compressively sensed signals using new chaotic system. Multimed Tools Appl 77, 31581–31606 (2018). https://doi.org/10.1007/s11042-018-6112-4

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  • DOI: https://doi.org/10.1007/s11042-018-6112-4

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