Skip to main content

Advertisement

SpringerLink
Log in

Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud systems are powerful computing resources used inevitably for data subscription and publication. Even though the cloud platform can handle the huge volume of data, privacy becomes a critical issue during data publishing. Hence, an effective technique for the privacy preservation of the data is required in the cloud computing environment. Accordingly, this paper proposes a technique for privacy protection using the dyadic product and an optimization algorithm. The privacy of the original database is protected by the construction of privacy preserved database using a dyadic square matrix obtained taking the dyadic product of two vectors, namely sensitive-utility (SU) coefficient and cumulative data key product. The selection of SU coefficient vector is based on the proposed (Crow search based Lion) C-Lion algorithm, which is designed by combining crow search algorithm with lion algorithm. The fitness of the proposed C-Lion algorithm is designed based on privacy and utility for the feasible selection of SU coefficient vector. The performance of the proposed privacy protection technique based on the C-Lion algorithm is evaluated using two factors, privacy, and utility. The experimental analysis shows that the proposed technique could attain the maximum utility of 0.909 with privacy 0.864 for the breast cancer dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Wang, C., Wang, Q., Ren, K., Lou, W.: Privacy- preserving public auditing for storage security in cloud computing. In: Proceedings of IEEE INFOCOM (2010)

  2. Bhagyashri, S., Gurav, Y.B.: Privacy-preserving public auditing for secure cloud storage. IOSR J. Comput. Eng. 16(4), 33–38 (2014)

    Article  Google Scholar 

  3. Satapathy, S.C., Bhateja, V., Raju, K.S., Janakiramaiah, B.: Computer communication. Networking and internet security. In: Proceedings of IC3T, vol. 5 (2016)

  4. Prakash, M., Singaravel, G.: An approach for prevention of privacy breach and information leakage in sensitive data mining. Comput. Electr. Eng. 45, 134–140 (2015)

    Article  Google Scholar 

  5. Torra, V.: Privacy in data mining. In: Handbook of Data Mining, Human Factor and Ergonomics (2009)

  6. Herranz, J., Nin, J., Rodrıguez, P., Tassa, T.: Revisiting distance-based record linkage for privacy-preserving release of statistical datasets. Data Knowl. Eng. 100, 78–93 (2015)

    Article  Google Scholar 

  7. Inan, A., Saygin, Y., Savas, E., Hintoglu, A., Levi, A.: Privacy preserving clustering on horizontally partitioned data. Data Knowl. Eng. 63(3), 646–666 (2007)

    Article  Google Scholar 

  8. Li, T., Li, N., Zhang, J., Molloy, I.: Slicing: a new approach for privacy preserving data publishing. IEEE Trans. Knowl. Data Eng. 24(3), 561–574 (2012)

    Article  Google Scholar 

  9. Pinkas, B.: Cryptographic techniques for privacy preserving data mining. ACM SIGKDD Explor. Newsl. 4(2), 12–19 (2002)

    Article  Google Scholar 

  10. Yuan, J., Yu, S.: Efficient privacy-preserving biometric identification in cloud computing. In: Proceedings of IEEE INFOCOM (2013)

  11. Li, M., Yu, S., Cao, N., Lou, W.: Authorized private keyword search over encrypted data in cloud computing. In: Proceedings of the 31st International Conference on Distributed Computing Systems (ICDCS) (2011)

  12. Karlekar, N.P., Gomathi, N.: Kronecker product and bat algorithm-based coefficient generation for privacy protection on cloud. Int. J. Model. Simul. Sci. Comput. 8(4), 1750021 (2017)

    Article  Google Scholar 

  13. Gehrke, J.: Models and methods for privacy-preserving data publishing and analysis. In: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 316–316 (2006)

  14. Yang, Z., Zhong, S., Wright, R.N.: Anonymity-preserving data collection. In: Proceedings of the 11th ACM SIGKDD Conference on Knowledge discovery in data mining, pp. 334–343 (2005)

  15. Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–69 (1965)

    Article  MATH  Google Scholar 

  16. Chaum, D.: Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM CACM 24(2), 84–90 (1981)

    Article  Google Scholar 

  17. Jakobsson, M., Juels, A., Rivest, R.L.: Making mix nets robust for electronic voting by randomized partial checking. In: Proceedings of the 11th USENIX Security Symposium, pp. 339–353 (2002)

  18. Fung, B., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14 (2010)

    Article  Google Scholar 

  19. Yang, K., Zhang, K., Jia, X., Hasan, M.A., Shen, X.S.: Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms. Inf. Sci. 387, 116–131 (2017)

    Article  MathSciNet  Google Scholar 

  20. Fahad, A., Tari, Z., Almalawi, A., Goscinski, A., Khalil, I., Mahmood, A.: PPFSCADA: privacy preserving framework for SCADA data publishing. Future Gener. Comput. Syst. 37, 496–511 (2014)

    Article  Google Scholar 

  21. Yang, K., Jia, X., Zhang, K., Shen, X.S.: Privacy-preserving data publish-subscribe service on cloud-based platforms. In: IACR Cryptology ePrint Archive, pp. 1–10 (2014)

  22. Zhang, H., Zhou, Z., Ye, L., Xiaojiang, D.U.: Towards privacy preserving publishing of set-valued data on hybrid cloud. IEEE Trans. Cloud Comput. 99, 1–14 (2015)

    Article  Google Scholar 

  23. Chandramohan, D., Vengattaraman, T., Dhavachelvan, P.: A secure data privacy preservation for on-demand cloud service. J. King Saud Univ.-Eng. Sci. 29(2), 144–150 (2017)

    Google Scholar 

  24. Kulkarni, Y.R., Murugan, T.S.: C-mixture and multi-constraints based genetic algorithm for collaborative data publishing. J. King Saud Univ.-Comput. Inf. Sci. (2016)

  25. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)

    Article  Google Scholar 

  26. Rajakumar, B.R.: Lion algorithm for standard and large scale bilinear system identification: a global optimization based on lion’s social behavior. In: IEEE Congress on Evolutionary Computation (CEC) July 6–11, Beijing, China (2014)

  27. Zhang, J.: Visualization for Information Retrieval, pp. 21–46. Springer, New York (2008)

    Book  Google Scholar 

  28. Vectors and dyadics .https://web.stanford.edu/class/me331b/documents/VectorBasisIndependent.pdf. Accessed July 2017

  29. Talk: Dyadic product. https://en.wikipedia.org/wiki/Talk%3ADyadic_product. Accessed July 2017

  30. Breast Cancer Wisconsin (Original) Data Set. https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original). Accessed July 2017

  31. Heart Disease Data Set. http://archive.ics.uci.edu/ml/datasets/heart+Disease. Accessed July 2017

  32. Pima Indian diabetes dataset. https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes. Accessed July 2017

  33. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Gonzalez, J.R. (ed.) Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Anna University, Chennai, Tamil Nadu, India

    Ashok George

  2. Adhiyamaan College of Engineering, Hosur, India

    A. Sumathi

Authors
  1. Ashok George
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Sumathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashok George.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

George, A., Sumathi, A. Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud. Cluster Comput 22 (Suppl 1), 1277–1288 (2019). https://doi.org/10.1007/s10586-017-1589-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1589-6

Keywords

Search

Navigation