Secure Mining of Generalized Association Rules from Horizontally Distributed Databases

We consider the problem of secure mining of generalized association rules from horizontally distributed databases. Given a large horizontally distributed database of transactions, where each transaction consists of a set of items and taxonomy on the items, we find associations between items at any level of the taxonomy. Generalized association rule mining technique has discussed in many papers. But, in this paper we discuss about secure mining of generalized association rules from horizontally distributed databases or homogeneous databases. For that purpose, we use the same privacy preserving distributed mining concepts discussed in paper [1] with the generalized association rule mining technique called ‘cumulate’ algorithm discussed in paper [2] . The main privacy preserving parts of the protocol in paper [1] are two secure multi-party algorithms called UNIFI and SETINC. Our proposed protocol is based on Fast Distributed Mining (FDM) algorithm. FDM algorithm is an unsecured distributed version of Apriori algorithm. It offers enhanced privacy, simplicity and efficiency. Keywords: Privacy Preserving Data Mining, Horizontally Distributed Databases, Generalized Association ruldes, Frequent Itemsets..

[1]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[2]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[3]  Alexandre V. Evfimievski,et al.  Privacy preserving mining of association rules , 2002, Inf. Syst..

[4]  Jaideep Vaidya,et al.  Privacy preserving association rule mining in vertically partitioned data , 2002, KDD.

[5]  Benny Pinkas,et al.  FairplayMP: a system for secure multi-party computation , 2008, CCS.

[6]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[7]  Ran Wolff,et al.  Privacy-preserving association rule mining in large-scale distributed systems , 2004, IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004..

[8]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[9]  Stan Matwin,et al.  Privacy-Preserving Collaborative Association Rule Mining , 2005, ICEB.

[10]  Tamir Tassa,et al.  Secure Mining of Association Rules in Horizontally Distributed Databases , 2011, IEEE Transactions on Knowledge and Data Engineering.

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

[12]  Stefan Rüping,et al.  Secure Distributed Subgroup Discovery in Horizontally Partitioned Data , 2011, Trans. Data Priv..

[13]  Jiawei Han,et al.  A fast distributed algorithm for mining association rules , 1996, Fourth International Conference on Parallel and Distributed Information Systems.

[14]  Rakesh Agrawal,et al.  Privacy-preserving data mining , 2000, SIGMOD 2000.