A fuzzy based scheme for sanitizing sensitive sequential patterns

The rapid advances in technology have led to generating and analyzing huge amounts of data in databases. The examples of such kind of data are bank records, web logs, cell phone records and network traffic records. This has raised a new challenge for people i.e., to transform this data into useful information. To achieve this task successfully, data mining is a vital technique. The aim of data mining is to extract knowledge from data. Sequential Pattern Mining (SPM) is an important area of data mining. Sequential data contains events and events contain items. The order between items does not matter. Whenever, we extract sequential information, there is always a threat that we may reveal sensitive sequential patterns. Thus, a need arises to protect sensitive sequential patterns. To fulfil this need; Privacy Preservation Data Mining (PPDM) techniques are used. The aim of privacy preservation techniques is to extract information from data without revealing sensitive information. In this research we would propose a technique based on FP growth approach and then applying anti-monotone and monotone constraints for identifying sensitive sequential patterns. For data modification we would apply the concept of fuzzy sets.