International Journal of Distributed and Cloud Computing

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

Privacy-Preservation in Collaborative Association Rule Mining for Outsourced Data


Affiliations
1 C.S.E. Department, S.G.S.I.T.S., Indore, Madhya Pradesh, India
2 C.T.A. Department, S.G.S.I.T.S., Indore, Madhya Pradesh, India
     

   Subscribe/Renew Journal


In recent years, the explosion of digital data and information, and various applications such as real-time monitoring, distributed collaboration, large-scale medical and financial data analysis and social network accumulate large amounts of data from different data owners. The burgeoning ability to generate vast volumes of data presents technical challenges for efficient data mining. Meanwhile, with the emergence of cloud computing and its model for IT services, which affords both computational and storage scalability, the outsourcing of data for storage and mining services is acquiring popularity. So, many organizations having insufficient storage and computational resources, willing to reduce their storage and computation cost, are widely adopting the outsourcing of the data mining jobs to a third party service provider. These service providers are assumed to be semi-trusted parties for privacy concerns. In this paper, we propose a collaborative privacy-preserving data mining (CPPDM) solution for outsourced data, which ensures that the data is stored, processed and shared without violating the user privacy. In our solution, we are using anonymization and encryption techniques for user privacy.

Keywords

CPPDM, Outsourcing, Privacy-Preservation, Semi-Trusted, Encryption, Anonymization, MapReduce.
Subscription Login to verify subscription
User
Notifications
Font Size


  • J. Vaidya, and C. Clifton, “Privacy-preserving data mining: Why, how, and when,” IEEE Security and Privacy, vol. 2, no. 6, 19-27, 2004.

  • C. H. Tai, P. S. Yu, and D. N. Yang et al., “Privacy-preserving social network publication against friendship attacks,” Proc. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’11), pp. 1262-1270, 2011.

  • E. Bertino, B. C. Ooi, Y. Yang, and R. H. Deng, “Privacy and ownership preserving of outsourced medical data,” Proc. of the 21st International Conference on Data Engineering (ICDE ’05), pp. 521-532, 2005.

  • S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava, “Privacy in dynamic social networks,” Proc. of the 19th International Conference on World Wide Web (WWW 10), pp. 1059-1060, 2010.

  • K. Chard, K. Bubendorfer, S. Caton, and O. F. Rana, “Social cloud computing: A vision for socially motivated resource sharing,” IEEE Transactions on Services Computing, vol. 5, no. 4, pp. 551-563, 2012.

  • S. Pallickara, J. Ekanayake, and G. Fox, “A scalable approach for the secure and authorized tracking of the availability of entities in distributed systems,” Parallel and Distributed Processing Symposium (IPDPS ’07), 1-10, 2007.

  • R. Aggarwal, A. Evmievski, and R. Srikant, Information sharing across private databases. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Database, pp. 86-97, San Diego, California, 2003.

  • Agrawal, S. and J. R. Haritsa, “A framework for high-accuracy privacy-preserving mining,” In Proceedings of the 21th IEEE International Conference on Data Engineering (ICDE 2005), pp. 193-204, Tokyo, Japan, 2005.

  • H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar, “Randomdata perturbation techniques and privacy-preserving data mining,” Knowledge and Information Systems: An International Journal, vol. 7, no. 4, pp. 387-414, 2005.

  • L. Qiu, Y. Li, and X. Wu, “An approach to outsourcing data mining tasks while protecting business intelligence and customer privacy,” In Work-shops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), pp. 551-558, Hong Kong, China, December 1822, 2006.

  • L. Qiu, Y. Li, and X. Wu, “Protecting business intelligence and customer privacy while outsouring data mining tasks,” Knowledge and Information Systems: An International Journal, Nov. 16, 2007.

  • B. Rozenberg, and E. Gudes, “Association rules mining in vertically partitioned databases,” Data Knowl. Eng., vol. 59, no. 2, pp. 378-396, 2006.

  • J. Zhan, S. Matwin, and L. Chang, “Privacy-preserving collaborative association rule mining,” In Proc. DBSEC, pp. 153-165, 2005.

  • S. Zhong, “Privacy-preserving algorithms for distributed mining of frequent itemsets, Inf. Sci., vol. 177, no. 2, pp. 490-503, 2007.

  • J. Vaidya, and C. Clifton, “Secure set intersection cardinality with application to association rule mining,” J. Comput. Secur., vol. 13, no. 4, pp. 593-622, 2005.

  • X. Ge, L. Yan, J. Zhu, and W. Shi, “Privacy-preserving distributed association rule mining based on the secret sharing technique,” In Proc. SEDM, pp. 345-350, Jun. 2010.

  • R. Kharat, M. Kumbhar, and P. Bhamre, “Efficient privacy preserving distributed association rule mining protocol based on random number,” In Intelligent Computing, Networking, and Informatics, pp. 827836, Raipur, Chhattisgarh, India: Springer, 2014.

  • K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis, “Security in outsourcing of association rule mining,” In Proc. VLDB, 111-122, 2007.

  • I. Molloy, N. Li, and T. Li, “On the (in)security and (im) practicality of outsourcing precise association rule mining,” In Proc. ICDM, Dec. 2009, pp. 872-877.

  • F. Giannotti, L. V. S. Lakshmanan, A. Monreale, D. Pedreschi, and W. Wang, “Privacy-preserving data mining from outsourced databases,” in Proc. CPDP, 2011, pp. 411-426.

  • F. Giannotti, L. V. S. Lakshmanan, A. Monreale, D. Pedreschi, and H. Wang, “Privacy-preserving mining of association rules from outsourced transaction databases,” IEEE Syst. J., vol. 7, no. 3, pp. 385-395, Sep. 2013.

  • B. Zhou, J. Pei, “Preserving privacy in social networks against neighborhood attacks,” Proc. 24th International Conference on Data Engineering (ICDE’ 08), pp. 506-515, 2008.

  • G. Piatetsky-Shapiro, “Discovery, analysis and presentation of strong rules,” Knowledge discovery in databases, pp. 229-238, 1991.

  • J. Pei, ”Preserving privacy in social networks against neighborhood attacks,” Proc. 24th International Conference on Data Engineering (ICDE ’08), pp. 506-515, 2008.


Abstract Views: 216

PDF Views: 7




  • Privacy-Preservation in Collaborative Association Rule Mining for Outsourced Data

Abstract Views: 216  |  PDF Views: 7

Authors

Khushbu Agrawal
C.S.E. Department, S.G.S.I.T.S., Indore, Madhya Pradesh, India
Vandan Tewari
C.T.A. Department, S.G.S.I.T.S., Indore, Madhya Pradesh, India

Abstract


In recent years, the explosion of digital data and information, and various applications such as real-time monitoring, distributed collaboration, large-scale medical and financial data analysis and social network accumulate large amounts of data from different data owners. The burgeoning ability to generate vast volumes of data presents technical challenges for efficient data mining. Meanwhile, with the emergence of cloud computing and its model for IT services, which affords both computational and storage scalability, the outsourcing of data for storage and mining services is acquiring popularity. So, many organizations having insufficient storage and computational resources, willing to reduce their storage and computation cost, are widely adopting the outsourcing of the data mining jobs to a third party service provider. These service providers are assumed to be semi-trusted parties for privacy concerns. In this paper, we propose a collaborative privacy-preserving data mining (CPPDM) solution for outsourced data, which ensures that the data is stored, processed and shared without violating the user privacy. In our solution, we are using anonymization and encryption techniques for user privacy.

Keywords


CPPDM, Outsourcing, Privacy-Preservation, Semi-Trusted, Encryption, Anonymization, MapReduce.

References