Abstract

Cloud computing is a current phrase in marketing for an idea which has been recognized for years: Outsourcing. Cloud computing provides a large amount of gratuities for each customer and enterprise agency. “Cloud model” is a more of a notion in which the data are hosted online and accessed in a time-anywhere manner, on a pay-per-use model. However, the users may not fully trust the cloud service providers (CSPs) in that environment. So, it is hard to decide whether the CSP meet their expectations to provide the proper secrecy to shared data. Moreover, in the environment of outsourcing, users have no longer control and ownership of data which may cause serious major issues related to data integrity. Previously, many researchers have committed themselves to draft auditing protocols for attaining proper public verification schemes through third-party auditor (TPA). On the other hand, these schemes may leak identity or data value to the third-party auditor. Therefore, to deal with these problems, we introduce an efficient public auditing protocol by constructing binary binomial tree (BBT)-like data structure with Boneh–Lynn–Shacham signature-based Homomorphic Verifiable Authenticator (BLS-HVA). This model also consists an index hash table (IHT), situated at TPA to record the information about the data block’s properties for auditing procedure. This model supports sampling blockless verification, batch auditing, and dynamic updating operations. Moreover, with such novel dynamic data structure, the proposed model guarantees that user’s group can easily trace any type of data changes through the designated BBT. Along with this, the users can also easily recover the accurate data blocks whenever the current data blocks are corrupted. The experimental results demonstrate that the proposed auditing model efficiently attains secure auditing for the cloud environment and outperforms the existing models in terms of communication and computation overhead.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call