Cloud Data Auditing Techniques with a Focus on Privacy and Security

Abstract

Storing large amounts of data with cloud service providers (CSPs) raises concerns about data protection. Data integrity and privacy can be lost because of the physical movement of data from one place to another by the cloud administrator, malware, dishonest cloud providers, or other malicious users who might distort the data.1 Hence, saved data corrections must be veri_ ed at regular intervals. Nowadays, with the help of cryptography, veri_ cation of remote (cloud) data is performed by third-party auditors (TPAs).2 TPAs are also appropriate for public auditing, o_ ering auditing services with more powerful computational and communication abilities than regular users.3 In public auditing, a TPA is designated to check the correctness of cloud data without retrieving the entire dataset from the CSP. However, most auditing schemes don’t protect user data from TPAs; hence, the integrity and privacy of user data are lost.1 Our research focuses on cryptographic algorithms for cloud data auditing and the integrity and privacy issues that these algorithms face. Many approaches have been proposed in the literature to protect integrity and privacy; they’re generally classi_ ed according to data’s various states: static, dynamic, multiowner, multiuser, and so on. We provide a systematic guide to the current literature regarding comprehensive methodologies. We not only identify and categorize the di_ erent approaches to cloud data integrity and privacy but also compare and analyze their relative merits. For example, our research lists the strengths and weaknesses of earlier work on cloud auditing, which will enable researchers to design new methods. Although related topics such as providing security to the cloud are beyond this article’s scope, cloud data auditing requires explicit a_ ention, which we provide below.

Existing System

Proposed System

Architecture