Fractional salp swarm algorithm: An association rule based privacy-preserving strategy for data sanitization

Abstract

BackgroundData mining is the process of extracting hidden patterns from huge repositories. Privacy preservation at the time of data shared for mining is a demanding dilemma. Conventional techniques, such as access control and authentication have been used to handle data privacy. Various data sanitization techniques like perturbation, generalization, and sampling are utilized to preserve confidential information from disclosure. MethodThis paper develops a Fractional-Salp swarm algorithm (Fractional-SSA) for data sanitization using privacy preserved data. The Fractional-SSA is developed by integrating Fractional calculus (FC) and the Salp swarm algorithm (SSA). The proposed Fractional-SSA hides sensitive rules considering a large transactional database and recovers the original database against malicious attacks. Here, the random key generation of the sanitization process is performed using the proposed Fractional-SSA algorithm by arbitrarily initializing various keys. In addition, the sanitized database is obtained from sanitization that derives association rules considering certain factors that involve privacy success, information preservation, hiding success, database difference, privacy loss, information loss, hiding failure, and database difference. Finally, the key value gets updated to estimate the best solution. The fitness is obtained using success and failure scenarios. ResultsThe proposed Fractional-SSA offered enhanced efficiency providing the highest privacy success of 0.933, information preservation of 0.487, minimal hiding failure of 0.054, and database difference of 0.008.