Biblio

In recent years, cloud computing has gained lots of importance and is being used in almost all applications in terms of various services. One of the most widely used service is storage as a service. Even though the stored data can be accessed from anytime and at any place, security of such data remains a prime concern of storage server as well as data owner. It may possible that the stored data can be altered or deleted. Therefore, it is essential to verify the correctness of data (auditing) and an agent termed as Third Party Auditor (TPA) can be utilised to do so. Existing auditing approaches have their own strengths and weakness. Hence, it is essential to propose auditing scheme which eliminates limitations of existing auditing mechanisms. Here we are proposing public auditing scheme which supports data dynamics as well as preserves privacy. Data owner, TPA, and cloud server are integral part of any auditing mechanism. Data in the form of various blocks are encoded, hashed, concatenated and then signature is calculated on it. This scheme also supports data dynamics in terms of addition, modification and deletion of data. TPA reads encoded data from cloud server and perform hashing, merging and signature calculation for checking correctness of data. In this paper, we have proposed efficient privacy preserving and dynamic public auditing by utilizing Merkle Hash Tree (MHT) for indexing of encoded data. It allows updating of data dynamically while preserving data integrity. It supports data dynamics operations like insert, modify and deletion. Several users can request for storage correctness simultaneously and it will be efficiently handled in the proposed scheme. It also minimizes the communication and computing cost. The proposed auditing scheme is experimented and results are evaluated considering various block size and file size parameters.

Most centralized systems allow data access to its cloud user if a cloud user has a certain set of satisfying attributes. Presently, one method to compete such policies is to use an authorized cloud server to maintain the user data and have access control over it. At times, when one of the servers keeping data is compromised, the security of the user data is compromised. For getting access control, maintaining data security and obtaining precise computing results, the data owners have to keep attribute-based security to encrypt the stored data. During the delegation of data on cloud, the cloud servers may be tampered by the counterfeit cipher-text. Furthermore, the authorized users may be cheated by retorting them that they are unauthorized. Largely the encryption control access attribute policies are complex. In this paper, we present Cipher-text Policy Attribute-Based Encryption for maintaining complex access control over encrypted data with verifiable customizable authorization. The proposed technique provides data confidentiality to the encrypted data even if the storage server is comprised. Moreover, our method is highly secured against collusion attacks. In advance, performance evaluation of the proposed system is elaborated with implementation of the same.

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. In this paper, we apply the ORAM algorithm to enable privacy-preserving access to big data that are deployed in distributed file systems built upon hundreds or thousands of servers in a single or multiple geo-distributed cloud sites. Since the ORAM algorithm would lead to serious access load unbalance among storage servers, we study a data placement problem to achieve a load balanced storage system with improved availability and responsiveness. Due to the NP-hardness of this problem, we propose a low-complexity algorithm that can deal with large-scale problem size with respect to big data. Extensive simulations are conducted to show that our proposed algorithm finds results close to the optimal solution, and significantly outperforms a random data placement algorithm.