Privacy Based Data Publishing Model for Cloud Computing Environment | Wireless Personal Communications
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Privacy Based Data Publishing Model for Cloud Computing Environment

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Abstract

Cloud computing is a popular model for providing data storage services from remote computing facilities through internet. Security is known as an element for protecting sensitive information from vulnerable attacks and ensuring information confidentiality, integrity and authenticity. Privacy is the assurance that users could maintain complete control over their sensitive information. Cloud storage-based data publication is significant in medical field where it contains sensitive information such as nature of the disease, patient medical history, and effects of the illness. The publisher should not disclose any of the individual or sensitive information of the individuals with the research board while publishing the reports to the medical data analysts. Deciding on the nature of sensitivity, the user may be allowed to access the information from cloud environment that is a complex process. In order to ensure the complete privacy of individual medical history, the present research work employs k-anonymization to upgrade the privacy policies in the cloud storage. In addition to this, the genetic grey wolf optimization algorithm is employed to decide the data to be published based on the information preserved for privacy purposes. The proposed work is evaluated in a real cloud infrastructure with respect to privacy, utility and information losses. The results show that the proposed method is efficient for privacy-based data publication as it conceals the sensitive information effectively.

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References

  1. Ari, A. A. A., et al. (2019). Enabling privacy and security in cloud of things: Architecture, applications, security and privacy challenges. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2019.11.005.

    Article  Google Scholar 

  2. Takabi, H., Joshi, J. B. D., & Ahn, G.-J. (2010). Security and privacy challenges in cloud computing environments. IEEE Security and Privacy,8(6), 24–31.

    Article  Google Scholar 

  3. Thokchom, S., & Saikia, D. K. (2020). Privacy preserving integrity checking of shared dynamic cloud data with user revocation. Journal of Information Security and Applications,50, 102427.

    Article  Google Scholar 

  4. Kundalwal, M. K., Chatterjee, K., & Singh, A. (2019). An improved privacy preservation technique in health-cloud. ICT Express,5(3), 167–172.

    Article  Google Scholar 

  5. Liang, K., et al. (2014). A DFA-based functional proxy re-encryption scheme for secure public cloud data sharing. IEEE Transactions on Information Forensics and Security,9(10), 1667–1680.

    Article  Google Scholar 

  6. Bibal-Benifa, J. V., & Dharma, D. (2018). A hybrid auto-scaler for resource scaling in cloud environment. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2018.04.016.

    Article  Google Scholar 

  7. Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks,76, 146–164.

    Article  Google Scholar 

  8. Raigoza, J., & Jituri, K. (2016). Evaluating performance of symmetric encryption algorithms. In 2016 international conference on computational science and computational intelligence (CSCI). https://doi.org/10.1109/csci.2016.0258.

  9. Sehgal, N. K., & Bhatt, P. C. P. (2018). Future trends in cloud computing. Cloud Computing. https://doi.org/10.1007/978-3-319-77839-6_12.

    Article  Google Scholar 

  10. Romanou, A. (2018). The necessity of the implementation of privacy by design in sectors where data protection concerns arise. Computer Law and Security Review,34(1), 99–110.

    Article  Google Scholar 

  11. Rosario, B., & Hearst, M. A. (2014). Classifying semantic relations in bioscience texts. In Proceedings of the 42nd annual meeting of the association for computational linguistics (ACL 2004). Association for Computational Linguistics 2004. http://biotext.berkeley.edu/dis_treat_data.html.

  12. Twenty Newsgroup Dataset. https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups.

  13. Reuters 21578 Dataset. https://archive.ics.uci.edu/ml/datasets/Reuters-21578+Text+Categorization+Collection.

  14. Caton, S., Bubendorfer, K., Chard, K., & Rana, O. F. (2012). Social cloud computing: A vision for socially motivated resource sharing. IEEE Transactions on Services Computing,5(4), 551–563.

    Article  Google Scholar 

  15. Liu, X., Zhang, Y., Wang, B., & Yan, J. (2013). Mona: Secure multi-owner data sharing for dynamic groups in the cloud. IEEE Transactions on Parallel and Distributed Systems,24(6), 1182–1191. https://doi.org/10.1109/tpds.2012.331.

    Article  Google Scholar 

  16. Chu, C.-K., Chow, S. S. M., Tzeng, W.-G., Zhou, J., & Deng, R. H. (2014). Key-aggregate cryptosystem for scalable data sharing in cloud storage. IEEE Transactions on Parallel and Distributed Systems,25(2), 468–477. https://doi.org/10.1109/tpds.2013.112.

    Article  Google Scholar 

  17. Cui, B., Liu, Z., & Wang, L. (2016). Key-aggregate searchable encryption (KASE) for group data sharing via cloud storage. IEEE Transactions on Computers,65(8), 2374–2385. https://doi.org/10.1109/tc.2015.2389959.

    Article  MathSciNet  MATH  Google Scholar 

  18. Sundareswaran, S., Squicciarini, A. C., & Lin, D. (2012). Ensuring distributed accountability for data sharing in the cloud. IEEE Transactions on Dependable and Secure Computing,9(4), 556–568.

    Article  Google Scholar 

  19. Shen, J., Zhou, T., Chen, X., Li, J., & Susilo, W. (2018). Anonymous and traceable group data sharing in cloud computing. IEEE Transactions on Information Forensics and Security,13(4), 912–925. https://doi.org/10.1109/tifs.2017.2774439.

    Article  Google Scholar 

  20. Abdel Hameed, S. A., Moussa, S. M., & Khalifa, M. E. (2019). Restricted sensitive attributes-based sequential anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Knowledge-Based Systems,164, 1–20.

    Article  Google Scholar 

  21. Wang, H. (2010). Privacy-preserving data sharing in cloud computing. Journal of Computer Science and Technology,25(3), 401–414. https://doi.org/10.1007/s11390-010-9333-1.

    Article  Google Scholar 

  22. Ding, W., Yan, Z., & Deng, R. (2017). Privacy-preserving data processing with flexible access control. IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/tdsc.2017.2786247.

    Article  Google Scholar 

  23. Zhang, X., Liu, C., Nepal, S., Pandey, S., & Chen, J. (2013). A privacy leakage upper bound constraint-based approach for cost-effective privacy preserving of intermediate datasets in cloud. IEEE Transactions on Parallel and Distributed Systems,24(6), 1192–1202. https://doi.org/10.1109/tpds.2012.238.

    Article  Google Scholar 

  24. Wang, B., Li, B., & Li, H. (2014). Oruta: Privacy-preserving public auditing for shared data in the cloud. IEEE Transactions on Cloud Computing,2(1), 43–56. https://doi.org/10.1109/TCC.2014.2299807.

    Article  MathSciNet  Google Scholar 

  25. Sanchez, R., Almenares, F., Arias, P., Diaz-Sanchez, D., & Marin, A. (2012). Enhancing privacy and dynamic federation in IdM for consumer cloud computing. IEEE Transactions on Consumer Electronics,58(1), 95–103. https://doi.org/10.1109/tce.2012.6170060.

    Article  Google Scholar 

  26. Mehta, K., Liu, D., & Wright, M. (2012). Protecting location privacy in sensor networks against a global eavesdropper. IEEE Transactions on Mobile Computing,11(2), 320–336. https://doi.org/10.1109/tmc.2011.32.

    Article  Google Scholar 

  27. Hassan, F., Domingo-Ferrer, J., & Soria-Comas, J. (2018). Anonymization of unstructured data via named-entity recognition. In International conference on modeling decisions for artificial intelligence (pp. 296–305). Cham: Springer.

  28. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems. https://doi.org/10.1186/2047-2501-2-3.

    Article  MATH  Google Scholar 

  29. Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,10(05), 571–588. https://doi.org/10.1142/s021848850200165x.

    Article  MathSciNet  MATH  Google Scholar 

  30. Cao, N., Wang, C., Li, M., Ren, K., & Lou, W. (2013). Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems,25(1), 222–233.

    Article  Google Scholar 

  31. Yang, X., Ma, T., Tang, M., & Tian, W. (2014). A survey of privacy preserving data publishing using generalization and suppression. Applied Mathematics and Information Sciences,8(3), 1103–1116.

    Article  Google Scholar 

  32. Hajian, S., Domingo-Ferrer, J., & Orio, F. (2014). Generalization-based privacy preservation and discrimination prevention in data publishing and mining. Data Mining and Knowledge Discovery,28(5–6), 1158–1188.

    Article  MathSciNet  MATH  Google Scholar 

  33. Kenig, B., & Tassa, T. (2012). A practical approximation algorithm for optimal k-anonymity. Data Mining and Knowledge Discovery,25(1), 134–168.

    Article  MathSciNet  MATH  Google Scholar 

  34. Singh, N., & Singh, A. K. (2018). Data privacy protection mechanisms in cloud. Data Science and Engineering,3(1), 24–39.

    Article  Google Scholar 

  35. Hua, J., Tang, A., Fang, Y., Shen, Z., & Zhong, S. (2016). Privacy-preserving utility verification of the data published by non-interactive differentially private mechanisms. IEEE Transactions on Information Forensics and Security,11(10), 2298–2311.

    Article  Google Scholar 

  36. Soria-Comas, J., Domingo-Ferrer, J., Sanchez, D., & Megias, D. (2017). Individual differential privacy: A utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security,12(6), 1418–1429.

    Article  Google Scholar 

  37. Kumar, P., & Alphonse, P. J. A. (2018). Attribute based encryption in cloud computing: A survey, gap analysis, and future directions. Journal of Network and Computer Applications,108, 37–52.

    Article  Google Scholar 

  38. Enamul, K. M., Wang, H., & Bertino, E. (2011). Efficient systematic clustering method for k-anonymization. Acta Informatica,48(1), 51–66.

    Article  MathSciNet  MATH  Google Scholar 

  39. Loukides, G., Gkoulalas-Divanis, A., & Malin, B. (2011). COAT: Constraint-based anonymization of transactions. Knowledge and Information Systems,28(2), 251–281.

    Article  Google Scholar 

  40. Centre for Advanced Computing and Research, Noorul Islam Centre for Higher Education. http://nichecloud.in/.

  41. Kulkarni, Y. R., & Murugan, T. S. (2018). C-mixture and multi-constraints based genetic algorithm for collaborative data publishing. Journal of King Saud University—Computer and Information Sciences,30(2), 175–184.

    Article  Google Scholar 

  42. Meyer-Baese A., & Schmid, V. (2014). In Pattern recognition and signal analysis in medical imaging (2nd edn).

  43. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software,69, 46–61.

    Article  Google Scholar 

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Acknowledgements

The support extended for the work in terms of computing facilities by Noorul Islam Centre for Higher Education, India is greatly acknowledged.

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Authors and Affiliations

  1. Indian Institute of Information Technology, Kottayam, India

    J. V. Bibal Benifa

  2. Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    G. Venifa Mini

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  1. J. V. Bibal Benifa
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Correspondence to J. V. Bibal Benifa.

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Bibal Benifa, J.V., Venifa Mini, G. Privacy Based Data Publishing Model for Cloud Computing Environment. Wireless Pers Commun 113, 2215–2241 (2020). https://doi.org/10.1007/s11277-020-07320-3

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