An Enhanced Data Anonymization Approach for Privacy Preserving Data Publishing in Cloud Computing Based on Genetic Chimp Optimization

Abstract

The data privacy. It is the biggest challenge in medical field to share and publish sensitive information about an individual to the cloud infrastructure. Therefore, it is essential to protect the patients’ information with high security and more data privacy. In this paper, a novel technique based on Mondrian based k-anonymization incorporated with Genetic-Chimp Optimization Algorithm is proposed to protect the privacy of the patients. The optimization algorithm employs average equivalence value and generalized information loss for the calculation of fitness value. Moreover, DNA-Genetic algorithm based encryption technique is also implemented after anonymization process to give extra protection to the anonymized database. The performance of the proposed privacy preservation technique is evaluated with respect to parameters such as information loss, privacy and utility. It can be observed that the proposed approach shows better results and it is efficient to preserve the privacy of medical databases when compared to other techniques.