Comparative Study of Privacy Preservation of Data in Hybrid Cloud of Combined Clustering and Geometric Data Perturbation Approach and Hybrid Ant Colony Optimization and Gravitational Search Algorithm

Abstract

Cloud computing services provide scalable IT infrastructural facilities to support the processing of various types of big data applications in sectors like health care and other businesses. Data sets like electronic health records contain private and sensitive information resulting in possible privacy issues. A simple method to facilitate data privacy preservation is to anonymize data through generalization to satisfy a given privacy model. However, majority of the existing privacy-preserving methods devised to small-scale data sets turn out to be inadequate in the case big data, due to their limitations or poor scalability. In this paper, two different approaches are discussed and their results are compared with the existing respective methods of Privacy Preservation of Health-care Data in the context of hybrid cloud. These two different approaches are: 1. A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Health-care Data in Hybrid Clouds and 2. Privacy Preservation of Health-care Data in Hybrid Cloud Using a Hybrid Meta-heuristics-based Sanitization Approach. This paper focuses on less-known aspects of side effects of the CCGDP approach which was addressed by ACOGSA approach.