Particle Swarm Intelligence and Impact Factor-Based Privacy Preserving Association Rule Mining for Balancing Data Utility and Knowledge Privacy

摘要

Organizations generally prefer data or knowledge sharing with others to obtain mutual benefits. The major issue in sharing the data or knowledge is data owners privacy requirements. Privacy preserving association rule mining is an area in which data owner can protect private association rules (sensitive knowledge) from disclosure while sharing the data. To safeguard sensitive association rules, individual data values of a database must be altered. Therefore, privacy concerns must not compromise data utility. A methodology that optimally selects and alters the transactions of the database is required to balance privacy and utility. Particle swarm optimization is a meta-heuristic technique used for optimization. Hence, an approach with particle swarm intelligence is developed to select a set of database transactions for alterations to minimize the number of non-sensitive association rules that are lost and to maintain high utility of the sanitized database without compromising on privacy concerns. The projected method for hiding association rules was assessed based on some performance parameters including utility of the transformed database. Experiments have revealed that the proposed method accomplished a good balance between privacy and utility by minimizing difference between original and transformed databases.