An Approach of Optimal Anonymization for Preserving Privacy in Cloud

Abstract

Cloud computing has emerged as a technological evolution in the recent past. It facilitates the provision of data storage, infrastructure and application. Organizations as well as Individuals uses the online services being provide by the cloud service provider. Organizations use data stored in the cloud environment for analysis and decision making purpose.In cloud-based environments protecting sensitive data is a challenge where the user does not have physical access control over the data and cloud infrastructure. This indicates that the cloud provider could be a very serious attack vector. Data protection and preservation is considered to be a significant concern in large applications in cloud environment which requires processing of large sensitive data sets. To address the need of protecting sensitive micro data of an individual, a technique called Data Anonymization like K-anonymization, L-diversity and T-closeness has been proposed by various researchers. Data deidentification and Re-identification meets the requirement for release of data for the research as well as for the protection of privacy of an individual. In this paper it is proposed to protect and preserve sensitive data of cloud users on a cloud based online Property Management application. k-Anonymization has a feature that in the dataset each tuple is indistinguishable from at least k-1 others. Anonymization methods i.e. K- anonymity, L-diversity and T-closeness have been used and the performance of these technique is evaluated in terms of optimal anonymity. We can find optimal Anonymization for the purpose of preserving privacy and reducing re-identification risk by experimenting on the real data. It is seen that Anonymization is useful in circumstance when the input data are processed for the purpose of preliminarily accessing an optimal anonymization.