Safeguarding Privacy for Cloud Computing's Encrypted Data Using Association Rule Mining Algorithm and FP Growth Algorithm

In an effort to support data privacy, safeguarding privacy guidelines for association mining techniques have recently been presented. Nevertheless, the algorithms are unable to conceal data frequency and have an extra expense when inserting fictitious objects or transactions. In this research, we present a connection constraint mining technique for cloud computing encrypted data that protects confidentiality. In order to accomplish the data mining operations using the two cloud servers, the strategy first uses propagated encryption, which impairs the cloud infrastructure’ encryption capabilities and shields the server from active attacks. Second, our approach uses combinations, coincidence masking, and cybernetic transactions in tandem to conceal the information while mining in order to safeguard the privacy of the association rule utilized in the extraction procedure. We use the FP growth algorithm for rule-mining for associations avoiding the need for extra fraudulent transactions. Therefore, the suggested technique can hide data frequency while ensuring query and data privacy. We show that, with regard to the time requirements of the Mining Association, the recommended strategy operates roughly three to five times better than the current algorithm.