Big Data Storage In Cloud Research Articles

Secured big data storage in cloud by intelligent authentication and privacy preservation via intelligent deep learning-aided heuristic strategy

Secured big data storage in cloud by intelligent authentication and privacy preservation via intelligent deep learning-aided heuristic strategy

Secured big data storage in cloud by intelligent authentication and privacy preservation via intelligent deep learning-aided heuristic strategy

Secured big data storage in cloud by intelligent authentication and privacy preservation via intelligent deep learning-aided heuristic strategy

Enhanced attribute based access control with secure deduplication for big data storage in cloud

The cloud storage is the best option to outsource big data, as the cloud has the capability of storing a huge volume of data. However, cloud storage brings new concerns for privacy, fine-grained access control and data duplication, which are crucial for big data storage in the cloud. Existing solutions of data duplication over encrypted data schemes do not provide fine-grained access control. Recently, Cui et al. proposed the data duplication over encrypted data along with attribute based access control in 2017. However, this scheme suffers from the following issues: 1) it does not verify the data ownership which is essentially required for data protection when multiple users outsource the same data 2) it does not provide the data ownership management, which creates a chance to upload the false data by the ownership revoked owner 3) it suffers from communication and computation overhead during deduplication and encryption process. To improve the Cui et al. scheme, in this paper, we propose an enhanced attribute based access control with secure deduplication for big data storage in cloud (EABAC-SD). Our EABAC-SD scheme achieves dynamic ownership management using the group key. Our scheme allows only authorized data owners to upload the data which enhances the security. Further, our EABAC-SD scheme cuts down the communication and computation overhead of the encryption and deduplication process. In addition, security analysis proves that our scheme protects the data privacy and consistency. Further, our scheme proves that ineligible data owners and ownership withdrawn data owners are not able to upload the data. Performance analysis shows that our EABAC-SD scheme is more efficient than Cui et al. scheme.

Efficient Escrow-free CP-ABE with Constant Size Ciphertext and Secret Key for Big Data Storage in Cloud

Ciphertext-policy attribute-based encryption (CP-ABE) schemes are an appropriate cryptographic technique to enable privacy along with access control in the cloud, but the existing CP-ABE schemes do not directly apply for big data because they have the issue of long ciphertext and long secret key size (LC-LS). To address LC-LS, the constant size ciphertext and secret key (CSC-S) schemes proposed. However, the existing CSC-S schemes suffer from the key escrow security issue and efficiency issue. To address both simultaneously, the authors propose an efficient escrow-free CP-ABE with constant size ciphertext and secret key (EEF-CPABE) for big data storage in the Cloud. The EEF-CPABE scheme reduces the encryption and decryption computation overhead by designing CSC-S. Further, the data owner generates the decryption global key to decrypt the data along with user secret key which solves the key escrow issue. Security and performance analysis demonstrate that the EEF-CPABE scheme resists against authority, and chosen plain-text attacks and more efficient than CSC-S schemes.

Intelligent cryptography approach for secure distributed big data storage in cloud computing

Implementing cloud computing empowers numerous paths for Web-based service offerings to meet diverse needs. However, the data security and privacy has become a critical issue that restricts many cloud applications. One of the major concerns in security and privacy is caused by the fact that cloud operators have chances to reach the sensitive data. This concern dramatically increases users’ anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issue and proposes an intelligent cryptography approach, by which the cloud service operators cannot directly reach partial data. The proposed approach divides the file and separately stores the data in the distributed cloud servers. An alternative approach is designed to determine whether the data packets need a split in order to shorten the operation time. The proposed scheme is entitled Security-Aware Efficient Distributed Storage (SA-EDS) model, which is mainly supported by our proposed algorithms, including Alternative Data Distribution (AD2) Algorithm, Secure Efficient Data Distributions (SED2) Algorithm and Efficient Data Conflation (EDCon) Algorithm. Our experimental evaluations have assessed both security and efficiency performances and the experimental results depict that our approach can effectively defend main threats from clouds and requires with an acceptable computation time.

WITHDRAWN: Dynamic remote data auditing for securing big data storage in cloud computing

Cloud computing has emerged as a new computing paradigm that offers great potential for storing data remotely. Presently, many organizations have reduced the burden of local data storage and maintenance by outsourcing data storage to the cloud. However, integrity and security of the outsourced data continues to be a matter of major concern for data owners due to the lack of control and physical possession over the data. To deal with this problem, researchers have proposed remote data auditing (RDA) techniques. However, the majority of existing RDA techniques is only applicable for static archived data and is not applicable for auditing or dynamically updating the outsourced data. They are also not applicable to big data storage because of the high computational overhead on the auditor. In this paper, we propose an efficient RDA technique based on algebraic signature properties for a cloud storage system that incurs minimum computational and communication costs. We also present the design of a new data structure-Divide and Conquer Table (DCT)—that can efficiently support dynamic data operations such as append, insert, modify, and delete. Our proposed data structure can be applied for large-scale data storage and will incur minimum computational cost. A comparison between our proposed method and other state-of-the-art RDA techniques shows that our method is secure and highly efficient in reducing the computational and communication costs on the server and the auditor.