Advertisement

The Journal of Supercomputing

, Volume 72, Issue 11, pp 4204–4220 | Cite as

Application of distributed parallel computing for dynamic visual cryptography

  • Raimondas Čiegis
  • Vadimas Starikovičius
  • Natalija Tumanova
  • Minvydas Ragulskis
Article

Abstract

We consider one applied global optimization problem where a set of feasible solutions is discrete and very large. The goal is to find optimal perfect gratings, which can guarantee high quality and security of the visual cryptography method. A priori estimation techniques, such as branch and bound type methods, cannot be applied to exclude an essential part of elements from the feasible set. Thus, a full search is required to solve this global optimization problem exactly, which is very computationally demanding. A library of C++ templates is developed that allows its user to implement parallel master–slave algorithms for his/her application without any knowledge of parallel programming API (application programming interface). Design of the templates allows users to build a parallel solver using MPI (message passing interface) API or distributed computing application using BOINC (Berkeley open infrastructure for network computing) API from the same C/C++ code with implementation of application-specific tasks. We build parallel and distributed computing solvers for the considered optimization problem and present results of computational experiments using a computer cluster and BOINC project for volunteer computing. Heuristic methods are also considered as an alternative to the full search algorithm. Due to complicated conditions defining feasible solutions (perfect gratings), genetic algorithms cannot be used to solve this problem efficiently. We propose two memetic heuristic algorithms, when a basic stochastic or simplified full search algorithm is combined with a local search algorithm. Parallel heuristic algorithms are also proposed and implemented. The efficiency and accuracy of heuristics are investigated and results of experiments are presented.

Keywords

Parallel algorithm Programming templates Distributed computing BOINC Dynamic visual cryptography  Heuristics 

Notes

Acknowledgments

The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, ‘Network for Sustainable Ultrascale Computing (NESUS)’. This research was also funded by a Grant NUMMOD (No. MIP-074/2015) from the Research Council of Lithuania. Computations were performed on resources at the High Performance Computing Center “HPC Sauletekis” in Vilnius University Faculty of Physics. The authors would like to thank the referees for their comments which are helpful and constructive.

References

  1. 1.
    Allen B et al (2013) The Einstein@Home search for radio pulsars and PSR J2007+2722 discovery. Astrophys J 773(2):91CrossRefGoogle Scholar
  2. 2.
    Anderson DP (2004) Boinc: a system for public resource computing and storage. In: Proceedings of the 5th IEEE/ACM international workshop on grid computing, pp 1–7Google Scholar
  3. 3.
    Baravykaite M, Čiegis R (2007) An implementation of a parallel generalized branch and bound template. Math Model Anal 12(3):277–289CrossRefzbMATHGoogle Scholar
  4. 4.
    Beberg AL, Ensign DL, Jayachandran G, Khaliq S, Pande VS (2009) Folding@Home: lessons from eight years of volunteer distributed computing. Parallel and Distributed Processing Symposium, International, pp 1–8Google Scholar
  5. 5.
    Blesa MJ, Hernàndez L, Xhafa F (2002) Parallel processing and applied mathematics: 4th international conference, PPAM 2001, Poland, September 9–12, 2001 Revised Papers, chap Parallel skeletons for Tabu search method based on search strategies and neighborhood partition, pp 185–193Google Scholar
  6. 6.
    Čiegis R, Starikovičius V, Tumanova N, Ragulskis M, Palivonaite R (2015) Distributed parallel computing for visual cryptography algorithms. In: Proceedings of the second international workshop on sustainable ultrascale computing systems (NESUS 2015), pp 23–28Google Scholar
  7. 7.
    Davies DW, Price WL (1989) Security for computer networks, 2nd edn. Wiley, New YorkGoogle Scholar
  8. 8.
    Depolli M, Trobec R, Filipič B (2013) Asynchronous master-slave parallelization of differential evolution for multi-objective optimization. Evol Comput 21(2):261–291CrossRefGoogle Scholar
  9. 9.
    Dorta I, Leon C, Rodriguez C, Rojas A (2003) Parallel skeletons for divide-and-conquer and branch-and-bound techniques. In: Proceedings of 11th Euromicro conference on parallel, distributed and network-based processing (Euro-PDP’03), IEEE, pp 292–298Google Scholar
  10. 10.
    Gerald CF, Wheatley PO (2004) Applied numerical analysis. Addison-Wesley, BostonzbMATHGoogle Scholar
  11. 11.
    Glover F, Kochenberger GA (2003) Handbook of metaheuristics, international series in operations research and management science. Springer, BerlinCrossRefGoogle Scholar
  12. 12.
    Goldberg D (2002) The design of innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers, DordrechtCrossRefzbMATHGoogle Scholar
  13. 13.
    Gonzalez-Velez H, Leyton M (2010) A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers. Softw Pract Exp 40(12):1135–1160CrossRefGoogle Scholar
  14. 14.
    Hegde C, Manu S, Shenoy PD, Venugopal K, Patnaik L (2008) Secure authentication using image processing and visual cryptography for banking applications. In: ADCOM 2008. 16th international conference on advanced computing and communications, IEEE, pp 65–72Google Scholar
  15. 15.
    Horst R, Pardalos PM, Thoai NV (2000) Introduction to global optimization, 2nd edn. Kluwer Academic Publishers, DordrechtCrossRefzbMATHGoogle Scholar
  16. 16.
    Korpela EJ (2012) SETI@Home, BOINC, and volunteer distributed computing. Annu Rev Earth Planet Sci 40(1):69–87CrossRefGoogle Scholar
  17. 17.
    Kumar V, Grama A, Gupta A, Karypis G (1994) Introduction to parallel computing: design and analysis of algorithms. Benjamin/Cummings, San FranciscozbMATHGoogle Scholar
  18. 18.
    Lewis A, Mostaghim S, Scriven I (2009) Asynchronous multi-objective optimisation in unreliable distributed environments. In: Lewis A, Mostaghim S, Randall M (eds) Biologically-inspired optimisation methods: parallel algorithms, systems and applications, studies in computational intelligence, vol 210. Springer, Heidelberg, pp 51–78Google Scholar
  19. 19.
    Menezes AJ, van Oorschot PC, Vanstone SA (1996) Handbook of applied cryptography. CRC Press, Boca RatonCrossRefzbMATHGoogle Scholar
  20. 20.
    Message Passing Interface Forum (1995) MPI: a message passing interface standard. http://www.mpi-forum.org
  21. 21.
    Mostaghim S, Branke J, Lewis A, Schmeck H (2008) Parallel multi-objective optimization using master–slave model on heterogeneous resources. In: Evolutionary computation, 2008. CEC 2008. IEEE world congress on computational intelligence, pp 1981–1987Google Scholar
  22. 22.
    Naor M, Shamir A (1995) Visual cryptography. In: De Santis A (ed) Advances in cryptology—EUROCRYPT’94, lecture notes in computer science, vol 950. Springer, Berlin, pp 1–12Google Scholar
  23. 23.
    Palivonaite R, Fedaravicius A, Aleksa A, Ragulskis M (2013) Near-optimal moire grating for chaotic dynamic visual cryptography. In: Zaman H, Robinson P, Olivier P, Shih T, Velastin S (eds) Advances in visual informatics, lecture notes in computer science, vol 8237. Springer International Publishing, Switzerland, pp 48–58CrossRefGoogle Scholar
  24. 24.
    Ragulskis M, Aleksa A (2009) Image hiding based on time-averaging moiré. Opt Commun 282(14):2752–2759CrossRefGoogle Scholar
  25. 25.
    Ragulskis M, Aleksa A, Maskeliunas R (2009) Contrast enhancement of time averaged fringes based on moving average mapping functions. Opt Lasers Eng 47(7–8):768–773CrossRefGoogle Scholar
  26. 26.
    Ross A, Othman A (2011) Visual cryptography for biometric privacy. IEEE Trans Inform Forensics Secur 6(1):70–81CrossRefGoogle Scholar
  27. 27.
    Starikovičius V, Čiegis R, Iliev O (2011) A parallel solver for the design of oil filters. Math Model Anal 16(2):326–341CrossRefzbMATHGoogle Scholar
  28. 28.
    Talbi EG, Mostaghim S, Okabe T, Ishibuchi H, Rudolph G, Coello Coello CA (2008) Parallel approaches for multiobjective optimization. In: Branke J, Deb K, Miettinen K, Slowinski R (eds) Multiobjective optimization: interactive and evolutionary approaches, lecture notes in computer science, vol 5252. Springer, Heidelberg, pp 349–372Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Raimondas Čiegis
    • 1
  • Vadimas Starikovičius
    • 2
  • Natalija Tumanova
    • 1
  • Minvydas Ragulskis
    • 3
  1. 1.Department of Mathematical ModelingVilnius Gediminas Technical UniversityVilniusLithuania
  2. 2.Laboratory of Parallel ComputingVilnius Gediminas Technical UniversityVilniusLithuania
  3. 3.Research Group for Mathematical and Numerical Analysis of Dynamical SystemsKaunas University of TechnologyKaunasLithuania

Personalised recommendations