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Research Article Open Access
Distributed data mining explores unknown information from data sources which are distributed among several parties. Privacy of participating parties becomes great concern and sensitive information pertaining to individual parties and needs high protection when data mining occurs among several parties. Different approaches for mining data securely in a distributed environment have been proposed but in the existing approaches, collusion among the participating parties might reveal responsive information about other participating parties and they suffer from the intended purposes of maintaining privacy of the individual participating sites, reducing computational complexity and minimizing communication overhead. The proposed method finds global frequent item sets in a distributed environment with minimal communication among parties and ensures higher degree of privacy with Data Encryption Standard (DES). The proposed method generates global frequent item sets among colluded parties without affecting mining performance and confirms optimal communication among parties with high privacy and zero percentage of data leakage.
Distributed data mining, privacy, secure multiparty computation, Frequent Item sets, Data Encryption standard (DES)., Aerospace Engineering,Applied Electronics,Applied Sciences,Biogenetic Engineering,Biomedical Engineering,Fluid Dynamics.