Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices

In this paper a homomorphic privacy preserving association rule mining algorithm is proposed which can be deployed in resource constrained devices (RCD). Privacy preserved exchange of counts of itemsets among distributed mining sites is a vital part in association rule mining process. Existing cryptography based privacy preserving solutions consume lot of computation due to complex mathematical equations involved. Therefore less computation involved privacy solutions are extremely necessary to deploy mining applications in RCD. In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets encrypted by all mining sites without revealing individual values. The proposed algorithm is built on with a well known communication efficient association rule mining algorithm named count distribution (CD). Security proofs along with performance analysis and comparison show the well acceptability and effectiveness of the proposed algorithm. Efficient and straightforward privacy model and satisfactory performance of the protocol promote itself among one of the initiatives in deploying data mining application in RCD.

[1]  A Rama Mohan Reddy,et al.  Parallel Privacy Preserving Association rule mining on pc Clusters , 2009, 2009 IEEE International Advance Computing Conference.

[2]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.

[3]  Alfred Menezes,et al.  Authenticated Diffie-Hellman Key Agreement Protocols , 1998, Selected Areas in Cryptography.

[4]  Ming-Syan Chen,et al.  Hardware-Enhanced Association Rule Mining with Hashing and Pipelining , 2008, IEEE Transactions on Knowledge and Data Engineering.

[5]  Chris Clifton,et al.  Privacy-Preserving Data Mining , 2006, Encyclopedia of Database Systems.

[6]  A. Yao,et al.  Fair exchange with a semi-trusted third party (extended abstract) , 1997, CCS '97.

[7]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[8]  Elisa Bertino,et al.  Association rule hiding , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Jinhua Guo,et al.  A Group Signature Based Secure and Privacy-Preserving Vehicular Communication Framework , 2007, 2007 Mobile Networking for Vehicular Environments.

[10]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..

[11]  Qi Wang,et al.  On the privacy preserving properties of random data perturbation techniques , 2003, Third IEEE International Conference on Data Mining.

[12]  Ralph Howard,et al.  Data encryption standard , 1987 .

[13]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[14]  Yehuda Lindell,et al.  Introduction to Modern Cryptography , 2004 .

[15]  Nigel Davies,et al.  Preserving Privacy in Environments with Location-Based Applications , 2003, IEEE Pervasive Comput..

[16]  Prem Prakash Jayaraman,et al.  Sensor Data Collection Using Heterogeneous Mobile Devices , 2007, IEEE International Conference on Pervasive Services.

[17]  Yanchun Zhang,et al.  Privacy-preserving distributed association rule mining via semi-trusted mixer , 2007, Data Knowl. Eng..

[18]  Sheng Zhong,et al.  Privacy-Preserving Classification of Customer Data without Loss of Accuracy , 2005, SDM.

[19]  Wen-Guey Tzeng,et al.  A Secure Fault-Tolerant Conference-Key Agreement Protocol , 2002, IEEE Trans. Computers.

[20]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.

[21]  Alexandre V. Evfimievski,et al.  Randomization in privacy preserving data mining , 2002, SKDD.