MINING OF ASSOCIATION RULES FROM DISTRIBUTED DATA USING MOBILE AGENTS

In this paper, we propose an agent-based approach to mine association rules from data sets that are distributed across multiple locations while preserving the privacy of local data. This approach relies on the local systems to find frequent itemsets that are encrypted and the partial results are carried from site to site. In this way, the privacy of local data is preserved. We present a structural model that includes several types of mobile agents with specific functionalities and communication scheme to accomplish the task. These agents implement the privacy-preserving algorithms for distributed association rule mining.