A pattern based framework for privacy preservation through association rule mining

Data mining extracts novel and useful knowledge from large repositories of data and has become an effective analysis and decision means in corporation. The sharing of data for data mining can bring a lot of advantages for research and business collaboration. The misuse of these techniques may lead to the disclosure of sensitive information. However, large repositories of data contain private data and sensitive rules that must be protected before published. Motivated by the multiple conflicting requirements of data sharing, privacy preserving and knowledge discovery, and privacy preserving data mining has become a research hotspot in data mining and database security fields. Researchers have recently made efforts at hiding sensitive association rules. This paper presents a novel based approach that strategically modifies a few transactions in the database. It modifies support or confidence values for hiding sensitive rules without producing many side effects. Nevertheless, undesired side effects such as nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process.