Abstract
Cloud computing refers to an information technology infrastructure where data and software are stored and processed in a remote data center, accessible as a service through the Internet. Typical data centers within these fields are large, complex and often noisy. Further-more, privacy preserving data mining is an important challenge. It is required to protect the confidentiality of data sources during the extraction of frequent closed patterns. In fact, no site should be able to learn contents of a transaction at any other site. The work carried out in this paper deals with this problem. In this context, we suggest an approach that combines the extraction of frequent closed patterns in a distributed environment such as the cloud. We aim at maintaining the privacy of the sites during the data mining task in a cloud environment based on homomorphic encryption. The Simulation results and performance analysis show that our mechanism requires less communication and computation overheads. It can effectively preserve data privacy, check data integrity, and ensures high data transmission efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Muller, S.D., Holm, S.R., Sondergaard, J.: Benefits of cloud computing: literature review in a maturity model perspective. Commun. Assoc. Inf. Syst. 37 (2015). Article no. 42
Hayward, R., Chiang, C.C.: Parallelizing fully homomorphic encryption for a cloud environment. J. Appl. Res. Technol. 13(2), 245–252 (2015). ISSN 1665-6423
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference In: KDD Conference, pp. 66–75 (2000)
Zitouni, M., Akbarinia, R., Ben Yahia, S., Masseglia, F.: A prime number based approach for closed frequent itemset mining in big data. In: 26th International Conference on Database and Expert Systems Applications, DEXA 2015 Valencia, Spain (2015)
Ben Yahia, S., Mephu Nguifo, E.: Approches d’extraction de règles d’association basées sur la correspondance de Galois. Ingénierie des systèmes d’information 9(3–4), 23–55 (2004)
Kumarn, D.S, Suneetha, C.H., Chandrasekhar, A.: Encryption of data using elliptic curve. Int. J. Distrib. Parallel Syst. (IJDPS) 3(1) (2012)
Gajbhiye, S., Karmakar, S., Sharma, M.: Diffie Hellman key agreement with elliptic curve discrete logarithm problem. Int. J. Comput. Appl. 129(12) (2015). (0975 8887)
Moumita, R., Nabamita, D., Jyoti, K.A.: Point generation and base point selection in ECC: an overview. Int. J. Adv. Res. Comput. Commun. Eng. 3(5) (2014)
Boneh, D., Gentry, C., Lynn, B., Shacham, H.: Aggregate and verifiably encrypted signatures from bilinear maps. In: Biham, E. (ed.) EUROCRYPT 2003. LNCS, vol. 2656, pp. 416–432. Springer, Heidelberg (2003). doi:10.1007/3-540-39200-9_26
Vassilios, S.V., Elisa, B., Igor, N.F., Loredana, P.P., Yucel, S., Yannis, T.: State of the art in privacy preserving data mining. SIGMOD Rec. 33, 50–57 (2004)
Wang, P.: Survey on privacy preserving data mining. Int. J. Digit. Content Technol. Appl. 4(9) (2010)
Thakur, D., Gupta, H.: An exemplary study of privacy preserving association rule mining techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(11) (2013). P.C.S.T., BHOPAL C.S Dept., India
Nithya, C.V., Jeyasree, A.: Privacy preserving using direct and indirect discrimination rule method. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(12) (2013). Vivekanandha College of Technology for Women Namakkal India
Lipmaa, H.: Cryptographic techniques in privacy preserving data mining, University College London, Estonian Tutorial (2007)
Hussien, A., Hamza, N., Hefny, H.: Attacks on anonymization-based privacy-preserving: a survey for data mining and data publishing. J. Inf. Secur. 4(2), 101–112 (2013)
Li, Y., Chen, M., Li, Q., Zhang, W.: Enabling multilevel trust in privacy preserving data mining. IEEE Trans. Knowl. Data Eng. 24(9), 1598–1612 (2012)
Li, X., Yan, Z., Zhang, P.: A review on privacy-preserving data mining. In: IEEE International Conference on Computer and Information Technology (CIT), pp. 769–774 (2014)
Kantarcioglu, M., Clifton, C.: Privacy preserving distributed mining of association rules on horizontally partitioned data. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 24–31 (2002)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644. ACM Press (2002)
Moez, W., Poncelet, P., Ben Yahia, S.: A novel approach for privacy mining of generic basic association rules. In: ACM First International Workshop on Privacy and Anonymity for Very Large Datasets, Join with CIKM 2009, France, pp. 45–52 (2009)
Canard, S., Desmoulins, N., Devigne, J., Le Hello, D.: Anonymisation des donnèes. Document de travail de l’objet de recherche: trust identity and privacy (2012)
Chang, X.-Y., Deng, D.-L., Yuan, X.-X., Hou, P.-Y., Huang, Y.-Y., Duan, L.-M.: Experimental realization of secure multi-party computation in an entanglement access network (2015)
Natarajan, R., Sugumar, R., Mahendran, M., Anbazhagan, K.: Design a cryptographic approach for privacy preserving data mining. Int. J. Innov. Res. Sci. Eng. Technol. 1(1) (2012)
Saxena, S., Kapoor, B.: State of the art parallel approaches for RSA public key based cryptosystem. Int. J. Comput. Sci. Appl. (IJCSA) 5(1) (2015)
Patel, S.J., Punjani, D., Jinwala, D.C.: An efficient approach for privacy preserving distributed clustering in semi-honest model using elliptic curve cryptography. Int. J. Netw. Secur. 17(3), 328–339 (2015)
Jitarwal, Y., Mangal, P.K., Suman, S.K.: Enhancement of elgamal digital signature based on RSA & symmetric key. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(5) (2015)
Okamoto, T., Uchiyama, S.: A new public key cryptosystem as secure as factoring. In: Proceedings of the Annals International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 1998), pp. 308–318 (1998)
Rathore, B.S., Singh, A., Singh, D.: A survey of cryptographic and non-cryptographic techniques for privacy preservation. Int. J. Comput. Appl. 130(13) (2015). (09758887)
Wong, W.K., Cheung, D.W., Hung, E., Kao, B., Mamoulis, N.: Security in outsourcing of association rule mining. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB), pp. 111–122 (2007)
Zhang, N., Li, M., Lou, W.: Distributed data mining with differential privacy. In: Proceedings of the IEEE International Conference on Communications (ICC), pp. 1–5 (2011)
Giannotti, F., Lakshmanan, L., Monreale, A., Pedreschi, D., Wang, H.: Privacy-preserving mining of association rules from outsourced transaction databases. IEEE Syst. J. 7(3), 385–395 (2013)
ftp://fpt2.cc.ukans.edu/pub/ippbr/census/pumps/pumbs90ks.zip
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hammami, H., Brahmi, H., Brahmi, I., Ben Yahia, S. (2017). Using Homomorphic Encryption to Compute Privacy Preserving Data Mining in a Cloud Computing Environment. In: Themistocleous, M., Morabito, V. (eds) Information Systems. EMCIS 2017. Lecture Notes in Business Information Processing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-65930-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-65930-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65929-9
Online ISBN: 978-3-319-65930-5
eBook Packages: Computer ScienceComputer Science (R0)