A Novel Data Partitioning Method for Active Privacy Protection Applied to Medical Records

In recent years, cloud computing has attracted extensive attention from industry and academia due to its convenience and ubiquity. As a new Internet-based IT service model, cloud computing has brought revolutionary changes to traditional computing and storage services. More and more individual users and enterprises are willing to deploy their own data and applications on the cloud platform, but the accompanying security issues have also become an obstacle to the development of cloud computing. Multi-tenancy and virtualization technologies are the main reasons why cloud computing faces many security problems. Through the virtualization of storage resources, multi-tenant data are generally stored as shared physical storage resources. To distinguish the data of different tenants, labels are generally used to distinguish them. However, this simple label cannot resist the attack of a potential malicious tenant, and data still has the risk of leakage. Based on this, this paper proposed a data partitioning method in a multi-tenant scenario to prevent privacy leakage of user data. We demonstrate the use of the proposed approach in protecting patient data in medical records in health informatics. Experiments show that the proposed algorithm can partition the attributes more fine-grained and effectively protect the sensitive information in the data.

Automated summary

Cloud computing has gained extensive attention due to its convenience and ubiquity. However, security issues, particularly related to multi-tenancy and virtualization, pose obstacles to its development. This paper proposes a data partitioning method to prevent privacy leakage in a multi-tenant scenario, specifically in protecting patient data in health informatics. Experimental results demonstrate the effectiveness of the proposed algorithm in fine-grained attribute partitioning and sensitive information protection.