Hybrid Solution for Privacy-Preserving Data Mining on the Cloud Computing

  • Huda OsmanEmail author
  • Mohd Aizaini Maarof
  • Maheyzah Md Siraj
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


The continuous evolution of the cloud has attracted many enterprises to outsource their data to the cloud to accomplish data mining tasks and other services. So that data owners can enjoy the benefits of the cloud without fear from violations of data privacy, PPDM approaches came to protect the privacy of data while preserving the usefulness of data. This article reviews the most popular models of PPDM over the cloud along with their strengths and the weaknesses to conclude the research gap. Some of the current PPDM models still vulnerable to various types of attacks like the K-anonymity model whereby surface from background knowledge attack, homogeneity attack, similarity attack and probabilistic inference attack, others of them consume great computational complexity, for example, the methods which depended on cryptography. The hybrid solution was proposed to protect the data privacy and overcome the current problems of PPDM include identify and attributes disclosures whereby preservation the privacy of data before to outsourced on the cloud is the main focus. K-Anonymization is combined with homomorphic encryption to rid from their limitations and take their advantage together to enhance data privacy-preserving and maintain the data utility of outsourced data.


Privacy-Preserving Data mining outsourcing Cloud data privacy 



We would like to thank Ministry of Higher Education and University of Kassala – SUDAN for funding this work.


  1. 1.
    Samanthula, B.K., Elmehdwi, Y., Jiang, W.: k-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)CrossRefGoogle Scholar
  2. 2.
    Suresh, S., Huang, H., Kim, H.J.: Scheduling in compute cloud with multiple data banks using divisible load paradigm. IEEE Trans. Aerosp. Electron. Syst. 51(2), 1288–1297 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhang, L., Yan, Y., Wang, W., Hao, X.: Finding quasi-identifiers for k-anonymity model by the set of cut-vertex (2018)Google Scholar
  4. 4.
    Zhang, H., Zhou, Z., Ye, L., Du, X.: Towards privacy preserving publishing of set-valued data on hybrid cloud (2018).
  5. 5.
    Aldeen Yousra, S., Mazleena, S.: A new heuristic anonymization technique for privacy preserved datasets publication on cloud computing. J. Phys: Conf. Ser. 1003, 012030 (2018)Google Scholar
  6. 6.
    Storagecraft Home: 7 Most Infamous Cloud Security Breaches (2016). Accessed 12 May 2019
  7. 7.
    Islam, S., Ouedraogo, M., Kalloniatis, C., Mouratidis, S., Gritzalis, S.: Assurance of security and privacy requirements for cloud deployment models (2018).
  8. 8.
    Chen, B., Cheung, P., Cheung, P., Kwok, Y.: CypherDB: a novel architecture for outsourcing secure database processing (2018).
  9. 9.
    Mendes, R., Ao, J.O.: Privacy-preserving data mining : methods, metrics, and applications. 5 (2017)Google Scholar
  10. 10.
    Reddy, S.R., Raju, K.V.S.V., Valli Kumari, V.: Personalized privacy preserving incremental data dissemination through optimal generalization. J. Eng. Appl. Sci. 13(11), 4205–4216 (2018)Google Scholar
  11. 11.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (1998)Google Scholar
  12. 12.
    Chakravorty, A., Wlodarczyk, T., Rong, C.: Privacy preserving data analytics for smart homes (2013)Google Scholar
  13. 13.
    Wang, J., Zhao, Y., Jiang, S., Le, J.: Providing privacy preserving in cloud computing. In: 3rd International Conference on Human System Interaction, HSI 2010 - Conference Proceedings (2010)Google Scholar
  14. 14.
    Mortazavi, R., Jalili, S.: Preference-based anonymization of numerical datasets by multi-objective microaggregation. Inf. Fusion 25, 85–104 (2015)CrossRefGoogle Scholar
  15. 15.
    Nayahi, J.J.V., Kavitha, V.: Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop. Future Gener. Comput. Syst. 74, 393–408 (2017)CrossRefGoogle Scholar
  16. 16.
    Rebollo-Monedero, D., Forné, J., Soriano, M., Allepuz, J.P.: p-Probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation. Inf. Sci. 382, 388–414 (2017)CrossRefGoogle Scholar
  17. 17.
    Jain, P., Gyanchandani, M., Khare, N.: Improved k-anonymity privacy-preserving algorithm using Madhya Pradesh State election commission big data. In: Studies in Computational Intelligence, vol. 771, pp. 1–10. Springer (2019)Google Scholar
  18. 18.
    Machanavajjhala, A., Gehrke, J., Kifer, D.: ℓ - diversity : privacy beyond k-anonymity, vol. V, pp. 1–47 (2006)Google Scholar
  19. 19.
    Domingo-Ferrer, J., Soria-Comas, J.: From t-closeness to differential privacy and vice versa in data anonymization. Knowl.-Based Syst. 74, 151–158 (2015)CrossRefGoogle Scholar
  20. 20.
    Abdelhameed, S.A., Moussa, S.M., Khalifa, M.E.: Privacy-preserving tabular data publishing: a comprehensive evaluation from web to cloud. Comput. Secur. 72, 74–95 (2018)CrossRefGoogle Scholar
  21. 21.
    Mayil, S., Vanitha, M.: A survey on privacy preserving data mining techniques for clinical decision support system 5(5), 6054–6056 (2016)Google Scholar
  22. 22.
    Uttarwar, N., Pradhan, M.A.: K-NN data classification technique using semantic search on encrypted relational data base. In: Proceedings - 2nd International Conference on Computing, Communication, Control and Automation, ICCUBEA 2016 (2017)Google Scholar
  23. 23.
    Omer, M.Z., Gao, H., Mustafa, N.: Privacy-preserving of SVM over vertically partitioned with imputing missing data. Distrib. Parallel Databases 35(3–4), 363–382 (2017)CrossRefGoogle Scholar
  24. 24.
    Selvaraj, B., Periyasamy, S.: A review of recent advances in Privacy preservation in health care data publishing. Int. J. Pharma Biosci. 7(4), 33–41 (2016)Google Scholar
  25. 25.
    Kaur, A., Sofat, S.: A proposed hybrid approach for privacy preserving data mining (2016)Google Scholar
  26. 26.
    González-Serrano, F.-J., Amor-Martín, A., Casamayón-Antón, J., Madrid, S.: Supervised machine learning using encrypted training data. Int. J. Inf. Secur. 17, 365–377 (2018)CrossRefGoogle Scholar
  27. 27.
    Li, L., Lu, R., Choo, K.K.R., Datta, A., Shao, J.: Privacy-preserving-outsourced association rule mining on vertically partitioned databases. IEEE Trans. Inf. Forensics Secur. 11(8), 1547–1861 (2016)Google Scholar
  28. 28.
    Aldeen, Y.A.A.S., Salleh, M.: A Hybrid K-anonymity Data Relocation Technique for Privacy Preserved Data Mining in Cloud Computing. J. Internet Comput. Serv. 17(5), 51–58 (2016)CrossRefGoogle Scholar
  29. 29.
    Kohlmayer, F., Prasser, F., Eckert, C., Kuhn, K.A.: A flexible approach to distributed data anonymization. J. Biomed. Inform. 50, 62–76 (2014)CrossRefGoogle Scholar
  30. 30.
    Yang, J.-J., Li, J.-Q., Niu, Y.: A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Gener. Comput. Syst. 43--44, 74–86 (2015)CrossRefGoogle Scholar
  31. 31.
    Taric, G.J., Poovammal, E.: A survey on privacy preserving data mining techniques. IOSR J. Comput. Eng. Vers. III 17(5), 2278–2661 (2017)Google Scholar
  32. 32.
    Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing. ACM Comput. Surv. 42(4), 1–53 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Huda Osman
    • 1
    • 2
    Email author
  • Mohd Aizaini Maarof
    • 1
  • Maheyzah Md Siraj
    • 1
  1. 1.Faculty of Engineering, School of ComputingUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of KassalaKassalaSudan

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