Dynamic Cloud Data Research Articles

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

Comments on a Dynamic Cloud Data Integrity Verification Scheme: Security Analysis and Improvement

Comments on a Dynamic Cloud Data Integrity Verification Scheme: Security Analysis and Improvement

Comments on a dynamic cloud data integrity verification scheme: security analysis and improvement

To ensure the integrity and reliability of the data on cloud servers, many public data verification schemes have been presented to allow data auditors to remotely audit the data integrity without fetching the data back. Recently, Zhao et al. (2021) proposed a dynamic data integrity audition scheme for cloud computing based on the dynamic array multi-branch tree (MBT). In their scheme, each leaf node x stores a hash value H(x) which relates to all the hash values of blocks in its block array. Because the value H(x) is always used to generate the integrity proof when any block linked to x is challenged, the attacker can deceive the auditor by replacing the challenged block with another one in the same block array. To address this problem of Zhao's scheme, we add a new index i for each block in the block array, which can ensure the uniqueness of each block to resist the replacement attack. Moreover, the algorithms of label generation and proof verification in Zhao's scheme are also upgraded to keep the soundness of the scheme. We give the security proof of the new scheme and make comprehensive performance analyses showing that compared with the original algorithms in Zhao's scheme, only negligible costs such as hash are brought to the new ones.

An Overview of the MPEG Standard for Storage and Transport of Visual Volumetric Video-Based Coding

The increasing popularity of virtual, augmented, and mixed reality (VR/AR/MR) applications is driving the media industry to explore the creation and delivery of new immersive experiences. One of the trends is volumetric video, which allows users to explore content unconstrained by the traditional two-dimensional window of director’s view.The ISO/IEC joint technical committee 1 subcommittee 29, better known as the Moving Pictures Experts Group (MPEG), has recently finalized a group of standards, under the umbrella of Visual Volumetric Video-based Coding (V3C). These standards aim to efficiently code, store, and transport immersive content with 6 degrees of freedom. The V3C family of standards currently consists of three documents: 1) ISO/IEC 23090-5 defines the generic concepts of volumetric video-based coding and its application to dynamic point cloud data; 2) ISO/IEC 23090-12 specifies another application that enables compression of volumetric video content captured by multiple cameras; and 3) ISO/IEC 23090-10 describes how to store and deliver V3C compressed volumetric video content. Each standard leverages the capabilities of traditional 2D video coding and delivery solutions, allowing for re-use of existing infrastructures which facilitates fast deployment of volumetric video.This article provides an overview of the generic concepts of V3C, as defined in ISO/IEC 23090-5. Furthermore, it describes V3C carriage related functionalities specified in ISO/IEC 23090-10 and offers best practices for the community with respect to storage and delivery of volumetric video.

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee effective resource provisioning. Objective: To tackle these challenges, future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on CPU utilization since it is considered a leading cause of the Quality of Service dropping. Methods: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that try to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and Conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for their strength in classification are also effective for prediction.

Integrated resource management pipeline for dynamic resource-effective cloud data center

Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

Multi-copy dynamic cloud data auditing model based on IMB tree

ABSTRACT In order to improve the efficiency and security of multi-copy dynamic cloud data audit, a multi-copy dynamic cloud data auditing model (MCAM) based on IMB tree is proposed. The IMB tree makes the audit process fast and efficient while maintaining security. Multi-copy compression transmission with masking rate is introduced to better reduce migration time. The theoretical analysis shows that MCAM can resist forgery attacks in the audit process and avoid information leakage. In addition, compared to the Merkle tree auditing solution, MCAM has improved data migration efficiency by about 17% and saved 24% of time consumption.

A modified Boneh-Lynn-Shacham signing dynamic auditing in cloud computing

Cloud Computing is an alternative to conventional IT Outsourcing. As a result, cloud computing migration between organizations is rapidly growing. The adoption of this technology brings many positive aspects but prescribes various risks and concerns. An organization that officially provides its cloud computing services to external providers and implies that its IT functions and process are outsourced to third-party providers of BPO services. For privacy-making public audit processes in dynamic cloud data storage, a modified Boneh-Lynn-Shachame Dynamic Auditing (MBLSDA) algorithm is suggested in this paper. The proposed algorithm executes an audit process for multiple users based on a batch audit simultaneously and effectively to enable Third Party Auditing (TPA). This paper integrates the homomorphic authenticator in an algorithm of dynamic signing audit by random marking in terms of the privacy conserving public auditing process. When the user of cloud service, store or update the data, it encrypts using Rijndal algorithm, and Boneh-Lynn-Shacham signature generation is used for key generation. Finally, a hybrid of these a proposed Modified Boneh-Lynn-Shacham Signing Dynamic audit the data to provide the security.

Privacy preserving integrity checking of shared dynamic cloud data with user revocation

In Cloud computing, storage and maintenance of the data are provided as service to the client. Traditionally, when the clients store and maintain their data at their own server, the data are dynamic in nature and are often shared among groups of users. The clients desire the traditional flexibility when they shift their data to the Cloud. Introducing the flexibility of sharing dynamic data with storage outsourced to the Cloud needs addressing new security challenges and requires the provision for user revocation. This paper presents a secure and effective scheme for storage of shared dynamic data in untrusted cloud servers with provisions for privacy preserving integrity check by third-party auditor and for addition and revocation of users. This proposed scheme is based on CDH-based ring signature and vector commitment.

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA ( Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport analytics application that has been developed on top of the EUBra-BIGSEA platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data analytics platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.

Efficient method to verify the integrity of data with supporting dynamic data in cloud computing

Efficient method to verify the integrity of data with supporting dynamic data in cloud computing

Secured data storage and auditing of data integrity over dynamic data in cloud

Secured data storage and auditing of data integrity over dynamic data in cloud

Public integrity auditing for shared dynamic cloud data

Cloud computing is an important storage platform being researched nowadays. It provides various services to its users. Among them, one of the salient service offered is cloud storage which makes data outsourcing a rising trend. But the major concern associated is the integrity and seclusion of outsourced data. Users require their data to be secure from any modification or unauthorized access. Therefore someway to verify whether the data is intact or not, without retrieving, should exist. This boosts the need of secure remote data auditing. This paper proposes an auditing scheme based on vector commitment, identity based ring signature and group key agreement protocol, emanated on bilinear pairing. An experimental analysis of the proposed scheme, later in the end, shows that when compared with its pertinent schemes, the proposed scheme is also efficient.

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

A Survey paper on Public Integrity Auditing for Shared Dynamic Cloud Data Using HMAC Algorithm

A Survey paper on Public Integrity Auditing for Shared Dynamic Cloud Data Using HMAC Algorithm

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

The advent of the cloud computing makes storage outsourcing become a rising trend, which promotes the secure remote data auditing a hot topic that appeared in the research literature. Recently some research consider the problem of secure and efficient public data integrity auditing for shared dynamic data. However, these schemes are still not secure against the collusion of cloud storage server and revoked group users during user revocation in practical cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and provide an efficient public integrity auditing scheme with secure group user revocation based on vector commitment and verifier-local revocation group signature. We design a concrete scheme based on the our scheme definition. Our scheme supports the public checking and efficient user revocation and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. Finally, the security and experimental analysis show that, compared with its relevant schemes our scheme is also secure and efficient.

Review of Public Integrity Auditing and Group User Revocation for Shared Dynamic Cloud Data

Review of Public Integrity Auditing and Group User Revocation for Shared Dynamic Cloud Data

A position-aware Merkle tree for dynamic cloud data integrity verification

In the cloud storage framework, once clients remotely store their data on cloud storage providers, they will lose the physical control over their outsourced data. The risk of unauthorized access to the data increases dramatically. One of the most serious problems in cloud storage is to ensure the correctness of the outsourced data. Specifically, we need to protect these data from unauthorized operations; we also need to detect and recover users' data after unexpected changes. In this paper, we propose a publicly verifiable scheme to protect the integrity of cloud data and support dynamic maintenance, which is based on a position-aware Merkle tree. We adopt a 3-tuple to define the node of the new Merkle tree, which records the position of the corresponding node, so that users can verify the consistency of the challenge-response blocks by computing the root value directly without retrieving the whole Merkle tree. In our scheme, the storage complexity at the client side is O(1); the computation complexity at the client side is $$O(\log n)$$O(logn); the computation cost at the server side is $$O(\log n)$$O(logn) and the communication overhead is $$O(\log n)$$O(logn). Our method supports unlimited verification challenges as well.