Build an Audit Framework for Data Privacy Protection in Cloud Environment

Abstract

With the rapid development of cloud computing technology, the data outsourcing service model in cloud environment is becoming increasingly popular. However, while cloud storage technology provides users with efficient services, its complex architecture also brings significant challenges to data privacy security. Because cloud service providers are not completely trusted, there is a risk of tampering or leaking user data, and third-party audits or malicious attacks by unauthorized users may also lead to data damage or loss. Therefore, how to realize the privacy protection and integrity audit of outsourced data in the cloud environment has become an important research topic of information security management. This paper focuses on the information security management method in the cloud environment, and deeply discusses the privacy protection and integrity audit technology of outsourced data. Aiming at the possible risk of tampering or leakage in the process of managing outsourced data by cloud service providers, a variety of privacy protection schemes based on data staining, data segmentation confusion and secure access control of private data are proposed. We study and propose a multi-copy integrity audit method that supports dynamic update of data, which realizes efficient dynamic operation and security verification through signature algorithm and random mask technology. We construct a privacy-protected data dynamic update integrity audit method, which utilizes hierarchical multi-branch tree data structure and random mask technology to significantly improve audit efficiency and security. In this paper, hash message authentication code (HMAC) and indistinguishable obfuscation (IO) techniques are used to propose a low-cost and efficient third-party audit scheme, which further reduces the computational overhead and verification cost.