An Enhanced Privacy Preservation Method Using Data Anonymization with Oppositional Fruit Fly Technique

Abstract

Abstract Nowadays, information is critical in every organization's decision-making process, especially in the business world. We are currently living in an information-processing civilization. Any organization or corporation relies on information about its most essential source of revenue. Every day, a large amount of sensitive information is generated by various corporate organizations such as banking, health care, and other similar organizations. This type of information is disseminated among numerous sources via various communication channels. As a result, safeguarding such sensitive information is critical in any data mining application. The data anonymization using the opposition fruitfly technique is described in detail in this article, including an upgraded privacy protection technique. For clustering, the suggested method uses an updated Fuzzy C-Means algorithm. The oppositional fruitfly algorithm, the proposed approach's privacy, has been validated for effectiveness. Comparing the suggested article with two existing methodologies demonstrates that the submitted article increased accuracy and privacy level. The suggested privacy preservation employing data anonymization methodology outperforms the existing method by 94.17% while the existing method outperforms it by 82.17%.