Impact of Opposition Intensity on Improved Cuckoo Search Algorithm for Privacy Preservation of Data

Abstract

Nowadays, large amounts of data are stored and retrieved frequently in day-to-day life.The data stored in the system may contain sensitive data which necessitates the implementation of preserving privacy in big data.For this reason, Privacy-Preserving Data Mining (PPDM) models are emerged to handle the privacy problems by avoiding unauthorized access and misuse if sensitive data.In current years, a lot of researches were implemented to handle the preservation issues.However, the challenges due to large data and computationally expensive issues limit the performance of PPDM approaches.Thus, this paper plans to implement a new PPDM model with two stages such as data sanitization and data restoration.In both the sanitizing and restoring process, key extraction is a major process that is optimally selected by means of modified CSA approach called Opposition Intensity-based Cuckoo Search Algorithm (OI-CSA).Finally, the performance of the proposed model is analyzed by varying co to 0.2, 0.4, 0.6, 0.8 and 1, respectively.