Secure and Efficient Association Rule Mining over Encrypted Cloud Data

Abstract

Association rule mining and frequent itemset mining are two approaches to data analysis that have garnered a lot of interest and have been subjected to a lot of research. These approaches can be used for a range of applications. In this effort, our primary focus is on mining encrypted cloud databases while maintaining strict adherence to privacy standards. The owners of the data in this scenario are interested in discovering the association rules or frequent item sets derived from a collective data collection, but they want to do so while limiting the amount of their sensitive raw data that is made available to other owners of the data and to third parties as much as is practically possible. This study looks at the topic of outsourcing jobs related to association rule mining and doing it in a cloud environment. The study does so within the context of various frameworks that are supposed to preserve corporate privacy. According to the findings of the study, there is a novel approach that can be used to guarantee that every item that is updated and sent from the data owner to the server can be interchanged without the attackers needing any further prior knowledge. Our methods are efficient, scalable, and safeguard users' privacy when applied to a transaction database that is both large and authentic. This database is a representation of our complete algorithm. In this technique, the final protocol for mining association rules is proposed, in addition to mining that protects users' privacy by using a cloud infrastructure that is both safe and resistant to data breaches.