No abstract

No abstract

No abstract

No abstract

With the advent of various technologies and digitization, popularity of the data mining has been increased for analysis and growth purpose in several fields. However, such pattern discovery by data mining also discloses personal information of an individual or organization. In today’s world, people are very much concerned about their sensitive information which they don’t want to share. Thus, it is very much required to protect the private data. This paper focuses on preserving the sensitive information as well as maintaining the efficiency which gets affected due to privacy preservation. Privacy is preserved by anonymization and efficiency is improved by optimization techniques as now days several advanced optimization techniques are used to solve the various problems of different areas. Furthermore, privacy preserving association classification has been implemented utilizing various datasets considering the accuracy parameter and it has been concluded that as privacy increases, accuracy gets degraded due to data transformation. Hence, optimization techniques are applied to improve the accuracy. In addition, comparison with the existing optimization technique namely particle swarm optimization, Cuckoo search and animal migration optimization has been carried out with the proposed approach specifically genetic algorithm for optimizing association rules.It has been concluded that the proposed approach requires more execution time about 20-80 milliseconds depending on the dataset but at the same time accuracy is improved by 5-6 % as compared to the existing approaches.