Privacy Protection of Class Association Rules produced by medical datasets

The enormous development in Information and Communications technology had increased the requirement for digital data to be used for medical purpose and to be saved and shared securely. This expansion of technology is proved to be very much useful for analysis and extracting various patterns from medical datasets. Classification technique followed by association rule mining is used for pattern extraction. This technique results in the if-then form of rules, which is simple to understand by end users. It is necessary to maintain privacy of individuals in medical dataset, as privacy is a major concern. This paper puts an effort on generation of class association rules and applying anonymization technique on it, to prevent its disclosure from the non-legitimate users.