PDDS - Improving cloud data storage security using data partitioning technique

Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.

As data sharing has become one of the most popular services offered by cloud storage, designing public auditing mechanisms for integrity of shared data becomes more important. Two problems which arise in shared data auditing include preserving users identity and collusion resistant revocation of users. When data stored at the cloud is shared among a group of users, different users may modify and sign different data blocks which leaks signer identities to the public verifier. Also, when a user is revoked from the group, signatures generated by this user should be re-signed by the cloud server using re-signature keys. In addition, collusion of cloud server and the revoked user should leak no information about the private key of other users. In this paper, by employing a new proxy re-signature scheme, we propose a public shared data auditing mechanism that provides identity privacy and collusion resistant user revocation, simultaneously. The proposed protocol requires only lightweight computations at the user side for signing data blocks in real-time online phase. Moreover, our protocol supports large dynamic group of users, batch auditing and dynamic data operations. Experimental results demonstrate excellent efficiency of our scheme in comparison to the state of the art.

Cloud storage relieves the user from the local management of software and hardware by enabling high quality on demand storage services to store their data in remote locations. Storing the data in remote locations not only reduces the management overheads but also eliminates the user’s physical control over data storage and data security. The loss of physical control over data brings out various security issues such as data loss, tampering of data, data modification. The primary issue is to ensure the correctness of data stored in remote locations which can be resolved by enabling public auditing for cloud storage services. In public auditing, not only the user but anyone can verify the data correctness. The introduction of third-party auditor, who works on behalf of cloud user to verify data integrity helps to reduce user computation resources and cost. The key prerequisite for the public auditing protocol is to support dynamic data operations as data in cloud are dynamic in nature. Cloud is a multiuser environment, so the auditing protocol must scale well to handle multiple auditing processes simultaneously. This paper aims at an extensive survey which mainly outlines the requirements for cloud public auditability and compares varied public auditing protocols.

In the HDFS cloud storage environment, the existing replica strategy is not able to adopt the optimal replica according to the current system environment dynamically, which affects the performance of the Cloud Storage system. This paper aims at the replica selection strategies in the load balancing problem, proposes a Replica Strategy based on grey prediction theory in Cloud Storage and predicts the node load rate of different replicas at a moment, selecting the minimum load node dynamically, eventually to load balance.

Cloud storage is one of the main application of the cloud computing. With the data services in the cloud, users is able to outsource their data to the cloud, access and share their outsourced data from the cloud server anywhere and anytime. However, this new paradigm of data outsourcing services also introduces new security challenges, among which is how to ensure the integrity of the outsourced data. Although the cloud storage providers commit a reliable and secure environment to users, the integrity of data can still be damaged owing to the carelessness of humans and failures of hardwares/softwares or the attacks from external adversaries. Therefore, it is of great importance for users to audit the integrity of their data outsourced to the cloud. In this paper, we first design an auditing framework for cloud storage and proposed an algebraic signature based remote data possession checking protocol, which allows a third-party to auditing the integrity of the outsourced data on behalf of the users and supports unlimited number of verifications. Then we extends our auditing protocol to support data dynamic operations, including data update, data insertion and data deletion. The analysis and experiment results demonstrate that our proposed schemes are secure and efficient.

Cloud Storage Service (CSS) is a vital service of cloud computing which relieves the burden of storage management, cost and maintenance. However, Cloud storage introduces new security and privacy challenges that make data owners worry about their data. It is essential to have an auditing service to verify the integrity of outsourced data and to prove to data owners that their data is correctly stored in the Cloud. Recently, many researchers have focused on validating the integrity of outsourced data and proposed various schemes to audit the data stored on CSS. However, most of those schemes deal with static and single copy data files and do not consider data dynamic operations on replicated data. Furthermore, they do not have the facility to repair corrupt data. In this paper, we address these challenging issues and propose a public auditing scheme for multiple-copy outsourced data in CSS. Our scheme achieves better reliability, availability, and scalability by supporting replication and data recovery.Keywordscloud storageauditingprobabilistic encryptionfountain codescryptographic hash algorithms

Quantiles are very useful in characterizing the data distribution of an evolving dataset in the process of data mining or network monitoring. The method of Stochastic Approximation (SA) tracks quantiles online by incrementally deriving and updating local approximations of the underly distribution function at the quantiles of interest. In this paper, we propose a generalization of the SA method for quantile estimation that allows not only data insertions, but also dynamic data operations such as deletions and updates.

With the rapid development of cloud storage technology, users choose to store their data in the cloud server remotely. Without the burden of local data storage and maintenance, users can enjoy on-demand high quality cloud storage services. Recently, lattice-based cryptography has been considered as the best choice for post-quantum cryptography, which can resist quantum computer attacks. Considering the forthcoming of the quantum computer in the near future, in this paper, we propose an efficient identity-based cloud storage public auditing scheme, which is constructed based on lattice. We prove our scheme can guarantee public verifiability, unforgeability. Moreover, our scheme can prevent the third party auditor TPA from revealing the primitive data blocks of cloud users. In particular, to achieve efficient data dynamics, by utilising index hash tables, our auditing scheme can efficiently perform dynamic operations. Efficient performance analysis demonstrates that our public auditing scheme is more efficient and more practical even in the post-quantum cryptographic era.

Cloud storage enables users to remotely store their data and benefit of the demand high quality cloud applications without the difficulty of local hardware and software management. Though the benefits are clear, such a service is also reliable to the users’ physical possession of their outsourced data, which inevitably poses new security risks towards the recovery of the data in cloud. In order to address this new problem and further achieve a secure and useful cloud storage service, we propose in this study a flexible distributed storage integrity mechanism, utilizing the homomorphic token and distributed data. The proposed design allows users to check the cloud storage with very lightweight communication and computation cost. The auditing result not only ensures strong cloud storage correctness guarantee, efficiency, but also simultaneously to access data error localization, i.e., the identification of misbehaving server. Considering the cloud data are dynamic in nature, the proposed design future supports secure and efficient dynamic operations on outsourced data, including block modification, update, deletion and append. The proposed scheme is highly efficient and secure against Byzantine failure, malicious data modification attack and even server colluding attacks.

To protect outsourced data in practical cloud storage against corruptions, enabling the integration of dynamic proof of retrievability (DPoR) and efficient recovery for cloud storage becomes significant and critical. However, in general PoR cloud storage, as all or part of data files are encoded, frequent or small updates require the updates of all related (encoded) file. Thus, this work studies the problem of constructing a novel regenerating code to be compatible with dynamic provable data possession (DPDP) into a DPoR system. Specifically, a class of exact-regenerating systematic code is presented to efficiently combine DPDP using the product matrix framework. In addition, a practical DPDP scheme is proposed and implemented for the specific regenerating code, while preserving the combined properties of default data integrity protection, efficient dynamic data updating, fault tolerance and repair traffic saving. Our DPDP scheme is based on the new Memory Adversary model specifically brought by dynamic operations. It allows different parameters to be fine-tuned for the performance-security tradeoff. We implement and evaluate the overhead of our DPDP scheme in cloud storage under different parameter choices. We demonstrate that DPDP can be feasibly integrated into our specific regenerating code.

More and more embedded devices, such as mobile phones, tablet PCs and laptops, are used in every field, so huge files need to be stored or backed up into cloud storage. Optimizing the performance of cloud storage is very important for Internet development. This paper presents the performance evaluation of the open source distributed storage system, a highly available, distributed, eventually consistent object/blob store from OpenStack cloud computing components. This paper mainly focuses on the mechanism of cloud storage as well as the optimization methods to process different sized files. This work provides two major contributions through comprehensive performance evaluations. First, it provides different configurations for OpenStack Swift system and an analysis of how every component affects the performance. Second, it presents the detailed optimization methods to improve the performance in processing different sized files. The experimental results show that our method improves the performance and the structure. We give the methods to optimize the object-based cloud storage system to deploy the readily available storage system.

NAND flash memory – ubiquitous in today’s world of smart phones, SSDs (solid state drives), and cloud storage – has a number of well-known reliability problems. NAND data contains bit errors, which require the use of error correcting codes (ECCs). The raw bit error rate (RBER) increases with program-erase (P-E) cycling, and the number of P-E cycles the device can withstand before the RBER exceeds the ECC capability is called its endurance . ECC operates on data stored in a sector of NAND, and there is a large variation in the endurance of sectors within a device and across devices, resulting in excessively conservative endurance specifications. This research shows, for the first time, that a sector’s true endurance can be predicted with remarkable accuracy, using a combination of the sector’s location within the device, and measurements taken at the very beginning of life. Real-world data is gathered on millions of NAND sectors using a custom-built test platform. Optimised machine learning classification models are built from the raw data to predict if a sector will pass or fail to a fixed ECC threshold, after a target P-E cycling level has been reached. A novel technique is demonstrated that uses different ECC thresholds for model training and testing, which allows the models to be tuned so that they never misclassify samples that would fail. This eliminates ECC failures and data loss, allowing simpler, less expensive ECC schemes to be used for modern NAND devices. It also enables significant endurance extensions to be achieved.

Objectives: Due to phenomenal growth of digital data and social media, industries need tool to migrate from traditional to agile approach with less investment to meet the requirements of customer. Statistical Analysis: In this paper, we have analyzed multiple research works and identified cloud storage is the most appropriate approach for digital universe. By understanding various techniques, we confirm that the common fear factors like data loss, personalization, security, reliability and availability have been completely addressed in cloud computing. Hence cloud storage can be suggested to industry for digital transformation Findings: There are 3 models in cloud computing and suitability of model based on need of industry requirement is not identified so far. We found open data source in hybrid cloud computing is the best arrangement for industry to fulfill the demand of various customers. Among the existing open databases Mongo DB is identified as faster and consistent. Application/Improvements: The proposed model suggests Mongo DB storage in Hybrid cloud computing to provide rich customer experience with less investment for industries. This need to be further analyzed with specific case studies in different types of organization using application tools.

Microsoft Orleans is a popular open source distributed programming framework and platform which invented the virtual actor model, and has since evolved into an actor-oriented database system with the addition of database abstractions such as ACID transactions. Properties of Orleans' virtual actor model imply that any ACID transaction mechanism for operations spanning multiple actors must support distributed transactions on top of pluggable cloud storage drivers. Unfortunately, distributed transactions usually perform poorly in this environment, partly because of the high performance and contention overhead of performing two-phase commit (2PC) on slow cloud storage systems. In this paper we describe the design and implementation of ACID transactions in Orleans. The system uses two primary techniques to mask the high latency of cloud storage and enable high transaction throughput. First, Orleans pioneered the use of a distributed form of early lock release by releasing all of a transaction's locks during phase one of 2PC, and by tracking commit dependencies to implement cascading abort. This avoids blocking transactions while running 2PC and enables a distributed form of group commit. Second, Orleans leverages reconnaissance queries to prefetch the state of all actors involved in a transaction from cloud storage prior to running the transaction and acquiring any locks, thus ensuring no locks are held while blocking on high latency cloud storage in most cases.

In cloud storage, replication technologies are essential to fault tolerance and high availability of data. While achieving the goal of high availability, replication brings extra number of active servers to the storage system. Extra active servers mean extra power consumption and capital expenditure. Furthermore, the lack of classification of data makes replication scheme fixed at the very beginning. This paper proposes an elastic and efficient file storage called E2FS for big data applications. E2FS can dynamically scale in/out the storage system based on real-time demands of big data applications. We adopt a novel replication scheme based on data blocks, which provides a fine-grained maintenance of the data in the storage system. E2FS analyzes features of data and makes dynamic replication decision to balance the cost and performance of cloud storage. To evaluate the performance of proposed work, we implement a prototype of E2FS and compare it with HDFS. Our experiments show E2FS can outperform HDFS in elasticity while achieving guaranteed performance for big data applications.