Survey on Secure Mining of Association Rules in Vertically Distributed Databases

A distributed database system is a collection of sites connected on a common high bandwidth network. Logically, data belongs to the same system but physically it is spread over the sites of the network, making the distribution invisible to the user. The advantage of this distribution resides in achieving availability, performance, modularity and reliability. In this paper, I have done a survey of papers related to Mining of Association Rules over distributed databases. From this survey, we have come up with a proposed solution to address the problem of secure mining of association rules where transactions are distributed in vertically distributed databases. Each site holds some attributes of each transaction and the sites wish to participate in the identification of globally valid association rules However, the sites should not reveal individual transaction data. The Protocol is based on Apriori Algorithm [2] and MultiParty Algorithm [3] for efficiently discovering frequent item sets with minimum support levels, without either site communicating individual transaction values. DOI: 10.17762/ijritcc2321-8169.15035

Interestingness measure on privacy preserved data with horizontal partitioning

Association rule mining is a process of finding the frequent item sets based on the interestingness measure. The major challenge exists when performing the association of the data where privacy preservation is emphasized. The actual transaction data provides the evident to calculate the parameters for defining the association rules. In this paper, a solution is proposed to find one such parameter i.e. support count for item sets on the non transparent data, in other words the transaction data is not disclosed. The privacy preservation is ensured by transferring the x-anonymous records for every transaction record. All the anonymous set of actual transaction record perceives high generalized values. The clients process the anonymous set of every transaction record to arrive at high abstract values and these generalized values are used for support calculation. More the number of anonymous records, more the privacy of data is amplified. In experimental results it is shown that privacy is ensured with more number of formatted transactions