Skip to main content
SpringerLink
Log in

Adaptive k-Anonymity Approach for Privacy Preserving in Cloud

  • RESEARCH ARTICLE - SPECIAL ISSUE - INTELLIGENT COMPUTING and INTERDISCIPLINARY APPLICATIONS
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Data privacy is very essential attribute for information sharing in cloud applications. Many end users find it challenging to adopt advanced technologies of cloud services, such as software-as-a-service, application-as-a-service, for protection of sensitive data in their health applications. Considerable research has been done in Privacy Preservation of sensitive data while ‘statistical analysis using data mining techniques’ such as k-anonymity being prominent. The term k-anonymity with respect to privacy of sensitive data is referred as sensitive information of an individual published and which cannot be distinguished from at least \(k-1\) individuals. k-Anonymity is achieved precisely with clustering techniques. However, the challenge is to find the best seed values for collecting allied records which can be anonymized at the same level in order to reduce information loss. This paper proposes a systematic approach for seed selection to cluster the records using the adaptive k-anonymity algorithm. A comparative study of recent works with a goal to reduce information loss and execution time is calibrated.

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. Yang, J.-J.; Jian-Qiang, L.; Niu, Y.: A hybrid solution for privacy preserving medical data sharing in the cloud environment. Futur. Gener. Comput. Syst. 43–44, 74–86 (2015)

    Article  Google Scholar 

  2. Zhang, X.; Liu, C.; Nepal, S.; Chen, J.: An efficient quasiidentifier index based approach for privacy preservation over incremental data sets on cloud. J. Comput. Syst. Sci. 79, 542–555 (2013)

    Article  Google Scholar 

  3. Xiao, X.K.; Tao, Y.F.: Anonymity: simple and effective privacy preservation. In: Proceedings of 32nd International Conference on Very Large Databases (VLDB ’06), pp. 139–150, Seoul, Korea (2006)

  4. Sweeney, L.: k-Anonymity: a model for protection privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  5. El Emam, K.; Dankar, F.K.: Protecting privacy using k-anonymity. J. Am. Med. Inform. Assoc. 15(5), 627–37 (2008)

    Article  Google Scholar 

  6. Gkoulalas-Divanis, A.; Loukides, G.; Sun, J.: Publishing data from electronic health records while preserving privacy: a survey of algorithms. J. Biomed. Inform. 50, 4–19 (2014)

    Article  Google Scholar 

  7. Hundepool, A.; Willenborg, L.: \(\mu \)- and \(\tau \)-argus, software for statistical disclosure control. In: Third International Seminar on Statistical Confidentiality. Bled, (1996)

  8. Sweeney, L.: Guaranteeing anonymity when sharing medical data, the Datafly system. J. Am. Med. Inform. Assoc. Washington, Hanley & Belfus, Inc., (1997)

  9. LeFevre, K.; DeWitt, D.J.; Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. 24th ACM SIGMOD International Conference on Management of Data, pp. 49–60. Baltimore, Maryland (2005)

    Google Scholar 

  10. Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

  11. LeFevre, K.; DeWitt, D.J.; Ramakrishnan, R.: Mondrian multidimensional \(k\)-anonymity. In: 22nd International Conference on Data Engineering (ICDE 06), p. 25, Atlanta, Georgia (2006)

  12. Li, J.Y.; Wong, R.C.; Fu, A.W.; Pei, J.: Achieving \(k\)-anonymity by clustering in attribute hierarchical structures. In: 8th International Conference on Data Warehousing and Knowledge Discovery, pp. 405–416, Krakow (2006)

  13. Ni, S.; Xie, M.; Qian, Q.: Clustering based k-anonymity algorithm for privacy preservation. Int. J. Netw. Secur. 19(6), 1062–1071 (2017)

    Google Scholar 

  14. Pierangela, S.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  15. Byun, J.-W.; Kamra, A.; Bertino, E.; Li, N.: Efficient \(k\)-anonymization using clustering techniques. In: International Conference on Database System for Advanced Applications (DASFAA), (2007)

  16. Loukides, G.; Shao, J.: Capturing data usefulness and privacy protection in \(k\)-anonymisation. In: Proceedings of the 2007 ACM Symposium on Applied Computing

  17. Janakiramaiah, B.; Reddy, A.R.M.: Privacy preserving association rule mining by concept of impact factor using item lattice. WSEAS Trans. Comput. 14, 567–581 (2014)

    Google Scholar 

  18. Bonam, J.; Reddy, R.: Balanced approach for hiding sensitive association rules in data sharing environment. Int. J. Inf. Secur. Priv. (IJISP) 8(3), 39–62 (2014). https://doi.org/10.4018/IJISP.2014070103

    Article  Google Scholar 

  19. Chiu, C.-C.; Tsai, C.-Y.: A \(k\)-anonymity clustering method for effective data privacy preservation. In: Third International Conference on Advanced Data Mining and Application (ADMA) (2007)

  20. Lin, J.-L.; Wei, M.-C.: An efficient clustering method for \(k\)-anonymization. In: International Workshop on Privacy and Anonymity in The Information Society, (2008)

  21. Lin, J.-L.; Wei, M.-C.; Li, C.-W.; Hsieh, K.-C.: A hybrid method for \(k\)-anonymization. In: 2008 IEEE Asia-pacific Services Computing Conference

  22. EnamulKabir, M.; Wang, H.; Bertinov, E.: Efficient systematic clustering method for k-anonymization. Acta Inform. 48, 51–66 (2011)

    Article  MathSciNet  Google Scholar 

  23. Pranamik, M.I.; Lau, R.Y.K.; Zhang, W.: \(k\)-anonymity through the enhanced clustering method. In: IEEE International Conference for e-Business Engineering, (2016)

  24. Abouelmehdi, K.; Beni-Hessane, A.; Khalouf, H.: Big healthcare data: preserving security and privacy. J. Big Data 5, 1 (2018)

    Article  Google Scholar 

  25. Xu, L.; Jiang, C.; Chen, Y.; Wang, J.; Ren, Y.: A framework for categorizing and applying privacy-preservation techniques in big data mining. IEEE Comput. Soc. 49(2), 54–62 (2016)

    Article  Google Scholar 

  26. Rong, H.; Wang, H.; Liu, J.; Xian, M.: Privacy-preserving \(k\)-nearest neighbor computation in multiple cloud environments. In: IEEE Access on Volume: 4

  27. Liu, F.; Li, T.: A clustering \(k\)-anonymity privacy-preserving method for wearable IoT devices. In: Hindawi, Security and Communication Networks, Wiley, (2018)

  28. Zheng, W.; Wang, Z.; Lv, T.; Ma, Y.; Jia, C.: \(k\)-Anonymity algorithm based on improved clustering. In: Vaidya, J.; Li, J. (Eds.) ICA3PP 2018, LNCS 11335, pp. 462–476. Springer Nature, Switzerland (2018)

  29. Aha, D.; Murphy, P.; Merz, C.; Keogh, E.; Blake, C.: Seth Hettich. David Newman, UCI Repository of Machine Learning Datasets (1987)

  30. Fung, B.C.M.; Wang, K.; Yu, P.S.: Top–down specialization for information and privacy preservation. In: 21st International Conference on Data Engineering, pp. 205–216, (2005)

  31. Lin, J.-L.; Wei, M.-C.: Genetic algorithm-based clustering approach for k-anonymization. Exp. Syst. Appl. 36, 9784–9792 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University College of Engineering Kakinada, UCEK(A), JNTUK, Kakinada, Andhra Pradesh, India

    Karuna Arava & Sumalatha Lingamgunta

Authors
  1. Karuna Arava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumalatha Lingamgunta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karuna Arava.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arava, K., Lingamgunta, S. Adaptive k-Anonymity Approach for Privacy Preserving in Cloud. Arab J Sci Eng 45, 2425–2432 (2020). https://doi.org/10.1007/s13369-019-03999-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-03999-0

Keywords

Search

Navigation