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Generation of Key Bit-Streams Using Sparse Matrix-Vector Multiplication for Video Encryption

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Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

Abstract

The contribution of stream ciphers to cryptography is immense. For fast encryption, stream ciphers are preferred to block ciphers due to their XORing operation, which is easier and faster to implement. In this paper we present a matrix-based stream cipher, in which a m \(\times \) n binary matrix single handedly performs the work of m parallel LFSRs. This can be treated as an equivalent way of generating LFSR-based stream ciphers through sparse matrix-vector multiplication (SpMV). Interestingly the output of the matrix multiplication can otherwise be used as a parallel bit/byte generator, useful for encrypting video streams.

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References

  1. Menezes, A.J., van Oorschot, P.C., Vanstone, S.A.: Handbook of Applied Cryptography. CRC Press LCC, Boca Raton (1996)

    Google Scholar 

  2. Dalai, D.K., Gupta, K.C., Maitra, S.: Results on algebraic immunity for cryptographically significant boolean functions. In: The Proceedings of Progress in Cryptology, INDOCRYPT 2004, vol. 3348, pp 92–106. Springer, LNCS (2004)

    Google Scholar 

  3. Ekdhal, P: On LFSR based stream ciphers analysis and design, Ph.D. Thesis, Department of Information Technology, Lund University (2003)

    Google Scholar 

  4. Frederik, Armknecht: Matthias, Krause: Constructing Single and Multi-output Boolean Functions with Maximal Algebraic Immunity, In: The Proceedings of ICALP 2006, Venice,Italy vol. 4052. Springer, LNCS (2006)

    Google Scholar 

  5. Golic, J.: Edit distances and probabilities for correlation attacks on clock controlled combiners with memory. In: ACISP 96, Wollongong, Australia. IEEE Trans. Inf. Theory 47(3), (2001)

    Google Scholar 

  6. Knuth, D.E.: The Art of Computer Programming, Combinatorial Algorithms, Part 1. Addison-Wesley Professional, New Jersey (2001)

    Google Scholar 

  7. Meier, W., Staffelbach, O.: Fast Correlation Attacks on Stream Ciphers. In: The Proceedings of Advances in Cryptography, EUROCRYPT’88, vol. 330, Springer, LNCS, Davos, Switzerland (1988)

    Google Scholar 

  8. Bell, N., Garland, M.: Effcient Sparse Matrix-Vector Multiplication on CUDA. NVIDIA Technical, Report, NVR-2008-004 (2008)

    Google Scholar 

  9. Lidl, R., Niederreiter, H.: Finite Fields, 2nd edn. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  10. Rueppel, RA.: Analysis and Design of Stream Ciphers. Springer, Berlin (1986)

    Google Scholar 

  11. Sarkar, P: Efficient implementation of large stream cipher systems. In: The Proceedings of Cryptographic Hardware and Embedded Systems, CHES 2001, vol. 2162, pp. 319–332, Springer, LNCS, Paris, France (2001)

    Google Scholar 

  12. Sarkar, P., Maitra, S.: Efficient implementation of cryptographically useful large boolean functions. IEEE Trans. Comput. 52, 410–417 (2003)

    Article  Google Scholar 

  13. Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., Demmel, J.: Optimization of sparse matrix-vector multiplication on emerging multicore platforms. In: The Proceedings of ACM/IEEE Conference on Supercomputing (2007)

    Google Scholar 

  14. Wong, A., Bishop, W.: An efficient, parallel multikey encryption of compressed video streams. In: The proceedings of International Conference on Signal and Image Processing (2006)

    Google Scholar 

  15. Yousef, S.: Iterative Methods for Sparse Linear Systems, 2nd edn. SIAM (2003)

    Google Scholar 

  16. Yzelman, A.N.: Fast sparse matrix-vector multiplication by partitioning and reordering. Ph.D. dissertation, Utrecht University (2011)

    Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    M. Sivasankar

Authors
  1. M. Sivasankar
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Correspondence to M. Sivasankar .

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Editors and Affiliations

  1. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    B. V. Babu

  2. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  5. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  6. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Kanad Ray

  7. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Umesh Gupta

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Sivasankar, M. (2014). Generation of Key Bit-Streams Using Sparse Matrix-Vector Multiplication for Video Encryption. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_82

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_82

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

  • eBook Packages: EngineeringEngineering (R0)

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