Data integrity auditing for secure cloud storage using user behavior prediction

Data integrity is a core security issue in reliable cloud storage that has received widespread attention. Data auditing protocols enable verifiers to efficiently check the integrity of out sourced data without downloading the data. A key research challenge associated with the design of existing data auditing protocols is the efficiency of the auditing process. Since existing protocols tend to auditing all cloud data, in fact, some data may have just been used or will be used soon, then auditing all these data is invalid, which is a waste of resources. In this paper, we attempt to address the waste of resources due to invalid auditing in cloud data integrity checking by introducing user behavior prediction algorithms,the first in such an approach, to the best of our knowledge. More specifically, we introduce the concept of valid auditing in data integrity verification based on the concept of valid auditing. We formalize a system model and a security model for this new concept. Then, using user behavior prediction algorithms, we give methods to improve auditing efficiency and reduce invalid auditing. Finally, we develop a prototype implementation of the protocol to validate the practicality of the scheme. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper focuses on the core security issue of data integrity in cloud storage, addressing the resource waste caused by invalid auditing through the introduction of user behavior prediction algorithms and proposing the concept of valid auditing. By formalizing a system model and security model, as well as developing a prototype implementation, it improves auditing efficiency and reduces invalid auditing.