ROUGH SET BASED PRIVACY PRESERVATION OF HEALTHCARE DATA USING ASSOCIATION RULE MINING AND GENETIC ALGORITHM

Smart healthcare refers to the combination of emerging trends like Big Data Analytics, IoT and AI & ML. The shift from traditional systems to smart health applications results in the generation of massive volumes of data that need to be managed. This data must be stored on cloud servers and shared with organizations for future research and applications. However, sharing it with third-party servers can result in the exposure of sensitive patient information to external entities. A significant challenge in this context is ensuring the protection of sensitive information to maintain privacy. Consequently, attribute reduction plays a crucial role in managing large datasets by removing unnecessary or redundant data, thereby facilitating the efficient hiding of sensitive rules before public disclosure. This paper presents a privacy-preserving framework designed to hide sensitive fuzzy association rules. The proposed model incorporates two key stages: a pre-activity phase for mining fuzzified association rules and a post-activity phase for concealing sensitive rules. Experimental findings confirm the effectiveness of the proposed approach.