ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Secure Association Rule Mining on Vertically Partitioned Data using Fully Homomorphic Encryption


Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
2 Department of Computer Science, G. Venkataswamy Naidu College, India
     

   Subscribe/Renew Journal


Cloud Computing is a leading innovation technology that guides to access applications over the web. The data owner’s data can be gotten to and controlled in the cloud. Privacy has become conclusive in information-driven applications within the distributed outsourced information. There are numerous inquiries still coming up on the best way to accomplish a confided in a climate that monitors application and information in the cloud from unapproved users. For offering protection to the users, sporadically there is a need to encode the data before accomplishing any other process. The cryptography method is embraced for data privacy. In this paper, a privacy-preserving construction is intended for vertically partitioned data in the cloud with the assistance of the Fully Homomorphic Encryption method. In this work, the homomorphic encryption and the Fully Homomorphic Encryption method is taken into consideration. The performance of the Rule Mining algorithm namely Eclat is compared with the encryption algorithms. The examination result shows that the Fully Homomorphic Encryption is less time-consuming to generate rule in the cloud, regardless of the number of transactions.

Keywords

Frequent Itemset Mining, Association Rule Mining, Homomorphic Encryption, Fully Homomorphic Encryption.
Subscription Login to verify subscription
User
Notifications
Font Size

  • V. Sathya and Dr. V. Gayathri, “Encryption-Based Techniques for Privacy Preserving Data Mining”, International Journal of Scientific and Engineering Research, Vol. 8, No. 4, pp. 52-56, 2017.

  • M. Kamber and J. Pei, “Mining Frequent Patterns, Associations and Correlations”, Morgan Kaufmaan Series, 2012.

  • M. Kaur and S. Kang, “Market Basket Analysis: Identify the Changing Trends of Market Data using Association Rule Mining”, Procedia Computer Science, Vol. 20, No. 1, pp.78-85, 2016.

  • A.M. Shahiri, W. Hussain and N.A. Rashid,” A Review on Predicting Student’s Performance using Data Mining Techniques”, Proceedings of 3rd International Conference on Information Systems, pp.414-422, 2015.

  • S.S Shengzhi0 and X. Cheng, “Differentially Private Frequent Itemset Mining via Transaction Splitting”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 7, pp.1875-1891, 2015.

  • Sandeep and Liji, “Secure Outsourced Association Rule Mining using Homomorphic Encryption”, International Journal of Engineering Research and Science, Vol. 3, No. 9, pp. 1-13, 2017.

  • R. Priya and Shweta, “A Tow Way Encryption for Privacy Preservation of Outsourced Transaction Database for Association Rule Mining”, International Journal of Scientific Research in Science and Technology, Vol. 4, No. 5, pp. 276-285, 2018.

  • Boris and Ehud, “Association Rules Mining in Vertically Partitioned Databases”, Data and Knowledge Engineering, Vol. 59, No. 2, pp. 378-396, 2006.

  • Xun and Tao, “More Efficient Fully Homomorphic Encryption Scheme Based on GSW and DM Scheme”, Security and Communication Networks, Vol. 2018, pp. 1-14, 2018.

  • Kenta and Masami, “Secure Association Rule Mining on Vertically Partitioned Data Using Private-set Intersection”, IEEE Access, Vol. 8, pp. 1-10, 2017.

  • N.V. Muthu Lakshmi and K. Sandhya Rani, “Privacy Preserving Association Rule Mining in Vertically Partitioned Databases”, International Journal of Computer Applications, Vol-39, pp.29-39, 2012.

  • K. Chavan, P. Kulkarni and P. Ghodekar, “Frequent Itemset Mining for Big Data”, Proceedings of International Conference on Green Computing and Internet of Things, Vol. 1, pp. 1365-1368, 2015.

  • Urvashi Garg, “Eclat Algorithm for Frequent Itemsets Generation”, International Journal of Computer Systems, Vol. 1, No. 3, pp. 82-84, 2014.

  • L. Li and R. Lu, “Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Database”, IEEE Transactions on Information Forensics and Security, Vol. 11, pp. 1847-1861, 2016.

  • Imran and Archana, “Homomorphic Encryption Applied to Cloud Computing”, International Journal of Information and Computation Technology, Vol. 4, No. 15, pp. 1519-1530, 2014.

  • S. Varma and P.I Liji, “Secure Outsourced Association Rule Mining using Homomorphic Encryption”, International Journal on Engineering Research, Vol. 3, No. 9, pp.70-76, 2017.

  • G. Kaosa, R. Paulet and X. Yi, “Secure Two-Party Association Rule Mining”, Proceedings of Australasian Conference on Security, pp.15-22, 2011.


Abstract Views: 260

PDF Views: 1




  • Secure Association Rule Mining on Vertically Partitioned Data using Fully Homomorphic Encryption

Abstract Views: 260  |  PDF Views: 1

Authors

M. Yogasini
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
B. N. Prathibha
Department of Computer Science, G. Venkataswamy Naidu College, India

Abstract


Cloud Computing is a leading innovation technology that guides to access applications over the web. The data owner’s data can be gotten to and controlled in the cloud. Privacy has become conclusive in information-driven applications within the distributed outsourced information. There are numerous inquiries still coming up on the best way to accomplish a confided in a climate that monitors application and information in the cloud from unapproved users. For offering protection to the users, sporadically there is a need to encode the data before accomplishing any other process. The cryptography method is embraced for data privacy. In this paper, a privacy-preserving construction is intended for vertically partitioned data in the cloud with the assistance of the Fully Homomorphic Encryption method. In this work, the homomorphic encryption and the Fully Homomorphic Encryption method is taken into consideration. The performance of the Rule Mining algorithm namely Eclat is compared with the encryption algorithms. The examination result shows that the Fully Homomorphic Encryption is less time-consuming to generate rule in the cloud, regardless of the number of transactions.

Keywords


Frequent Itemset Mining, Association Rule Mining, Homomorphic Encryption, Fully Homomorphic Encryption.

References