Secure Cloud Storage with Deduplication 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.

According to Cisco Global Cloud Index, cloud storage users will store 1.6 Gigabytes data per month by 2019, compared to 992 megabytes data per month in 2014. With this trend, it has been shown that more and more data will reside in cloud storage and it is expected to grow further. As cloud storage is becoming an option for users for keeping their data online, it comes with security concerns for protecting data from threats. This thesis addresses the need to investigate the security factors that will enable efficient security protection for data in cloud storage and the relationships that exist between the different security factors. Consequently, this research has developed a conceptual framework that supports security in cloud storage. The main contribution of this research is the development of a Cloud Storage Security Framework (CSSF) to support an integrative approach to understanding and evaluating security in cloud storage. The framework enables understanding of the makeup of security in cloud storage and measures the understanding of security in cloud storage. Drawing upon established theories and prior research findings, the framework indicates that security in cloud storage can be determined by nine factors: (1) security policies implementation in cloud storage, security measure that relates to (2) protecting the data accessed in cloud storage; (3) modifications of data stored; (4) accessibility of data stored in cloud storage; (5) non-repudiation to the data stored; (6) authenticity of the original data; (7) reliability of the cloud storage services; (8) accountability of service provision; and (9) auditability of the data accessed and stored in cloud storage. An example of CSSF application has been demonstrated through the development of a measuring instrument called Security Rating Score (SecRaS) and through a series of experiments, SecRaS has been validated and used in a research scenario. The instrument consists of several items generated using goal-question-metric approach. These potential items were evaluated by a series of experiments; the security experts assessed using content validity ratio while the security practitioners took part in the validation study. The validation study completed two experiments that look into the correlation analyses and internal reliability. SecRaS instrument was later applied in a research scenario; the validated instrument was distributed and a number of 218 usable responses were received. Using structural equation modelling, the data has revealed a good fit of the measurement analyses and structural model. The key findings were as follow: the relationships between factors were found to have both direct and indirect effects in the result. While establishing the relationship(s) among the factors, the structural model proposes three types of causal relationships in terms of how the security implementation in cloud storage could be affected by the security factors. This thesis presents a detailed discussion of the CSSF development, confirmation, and application in a research scenario. For security managers, CSSF offers a new paradigm on how stakeholders can make cloud storage security implementation successful in some depth. For security practitioners, the CSSF enables deconstruction of the concept of security in cloud storage into smaller, conceptually distinct and manageable factors to guide the design of security in cloud storage. For researchers, the CSSF provides a common framework in which to conceptualise their research and make it easier to see how the security factors fit into the larger picture.

An advancement in computing qualities that are both economical and powerful have been made possible by the Internet's dramatic development in processing and storage methods. A new technology that offers internet application access and data storage, cloud computing is becoming more and more popular. Both opportunities and challenges abound in this system. Two extremely important issues that need to be addressed are data security and the growing amount of similar data duplication in cloud. A cloud computing system, supported via distributed & virtual machine technology, offers the most comprehensive services in the most effective way. When using the cloud technology, customers may access their limitless storage space from any location at any time. Cloud service providers are responsive to customer requests to increase data storage capacity by integrating cloud systems with deduplication techniques, while deduplication techniques erase redundant data that is available in cloud environments, according to the company. Preserving data privacy is another significant issue that needs to be acknowledged for its significance. For reducing duplicate data quantity in storage system, the deduplication technique was developed. This study offers a novel method for eliminating duplicate or repetitive information from cloud servers, that might aid in lowering storage space amount and needed bandwidth. It was illustrated that by using testing outcomes, proposed model eliminates the main drawbacks of the existing methods while also offering increased security for data kept in cloud storage

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Cloud computing provides a scalable platform for large amount of data and processes that work on various applications and services by means of on-demand service. The storage services offered by clouds have become a new profit growth by providing a comparable cheaper, scalable, location-independent platform for managing users’ data. The client uses the cloud storage and enjoys the high end applications and services from a shared group of configurable computing resources using cloud services. It reduces the difficulty of local data storage and maintenance. But it gives severe security issues toward users’ outsourced data. Data Redundancy promotes the data reliability in Cloud Storage. At the same time, it increases storage space, Bandwidth and Security threats due to some server vulnerability. Data Deduplication helps to improve storage utilization. Backup is also less which means less Hardware and Backup media. But it has lots of security issues. Data reliability is a very risky issue in a Deduplication storage system because there is single copy for each file stored in the server which is shared by all the data owners. If such a shared file/chunk was missing, large amount of data becomes unreachable. The main aim of this work is to implement Deduplication System without sacrificing Security in cloud storage. It combines both Deduplication and convergent key cryptography with reduced overhead.

In today's digital era, the exponential growth of data necessitates effective storage and management solutions. The cloud has vast storage possibilities to store huge amounts of data. Public access to the cloud leads to duplicate copies of data stored in the storage. Maintaining a single copy of data in the cloud is most important for efficient data storage management. This paper introduces a groundbreaking strategy for improving the efficacy of cloud storage through innovative data deduplication techniques at the block levels. The block-level duplication verification efficiently identifies the duplicate data in the storage. It helps to protect the duplicate storage in the cloud data storage infrastructure. The block-level deduplication technique uses variable-length blocks based on the duplicate content of the block. Initially, A file is divided into a number of blocks with a size of 5kb. According to the proposed method, If any block is partially matched with a block already stored in the cloud, then that block is further divided into smaller blocks based on the matching percentage. The smaller blocks help to deduplicate the data more effectively. The work is implemented in a live cloud setting with a C# application hosted on MyASP.NET. The proposed methodology's effectiveness is validated against existing deduplication techniques. The results reveal a marked improvement in storage utilization and data management, affirming the potential of the approach to revolutionize cloud storage efficiency.

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

The cloud is the most suitable platform for storing and processing of data. Users are outsourcing data to the cloud to reduce the storage and maintenance cost locally. However, there are challenges to both cloud users as well as to the cloud service provider. The challenge for cloud service providers is to optimize the associated cost for the management and maintenance of uploaded data. The challenge for users is data privacy. To address these issues, encrypted data deduplication schemes proposed, but existing schemes suffer from security weakness and do not support data access control. In this paper, we propose an efficient ciphertext policy attribute-based encryption scheme for deduplication over encrypted data (ECP-ABE) in the cloud storage. Our scheme achieves privacy,deduplication along with access controls. The security and performance analysis demonstrates that our scheme is secure and efficient.

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc. The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. De Maio et al in the first paper1 “Social Media Marketing through Time-Aware Collaborative Filtering,” define a time-aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. The authors present experimental results comparing these techniques and performing model training in different time windows. The second paper2 “SERNOTATE: An Automated Approach for Business Service Description Annotation for efficient Service Retrieval and Composition” by Chotipant et al proposes an automated approach, namely, SERNOTATE, for business service description annotation for efficient service retrieval and composition. To that end, the authors employ new semantic-based linking approaches, namely, Extended Case-based Reasoning, Vector-based and Classification-based, that automatically annotate business services to relevant service concepts. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset. In the third paper,3 “Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval,” the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper,4 “Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for Big Data Transfer,” deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc. The last paper5 “Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto-system. Based on this algorithm, the authors transform a variant of delegated private set intersection protocol into an improved outsourced one, which enables the clients to perform only simple modular multiplication for computing during the execution of the protocol. Compared with the state of the art, the proposed protocol has a great advantage in efficiency. The editor of this special issue would like to thank all authors for their contributions and the reviewers for their constructive and useful feedback to authors. I would like to thank Prof Geoffrey Fox, the Editor-in-Chief of Concurrency and Computation: Practice and Experience, for the opportunity to edit this special issue and his support.

An increasing number of applications are being hosted on cloud based platforms. Cloud platforms are serving as a general computing facility and applications being hosted on these platforms range from simple multi-tier web applications to complex social networking, eCommerce and Big Data applications. High availability, performance and auto-scaling are key requirements of Cloud based applications. Cloud platforms serve these requirements using dynamic provisioning of resources in on-demand, multi-tenant fashion. A key challenge for cloud service providers is to ensure the Quality of Service (QoS), as a user / customer requires more explicit guarantees of QoS for provisioning of services. Cloud service performance problems can directly lead to extensive financial loses. Thus, control and verification of QoS become a vital concern for any production level deployment. Therefore, it is crucial to address performance as a managed objective. The success of cloud services depends critically on automated problem diagnostics and predictive analytics enabling organizations to manage their performance proactively. Moreover, effective and advance monitoring is equally important for performance management support in clouds. In this thesis, we explore the key techniques for developing monitoring and performance management systems to achieve robust cloud systems. At first, two case studies are presented as a motivation for the need of a scalable monitoring and analytics framework. It includes a case study on performance issues of a software service, which is hosted on a virtualized platform. In the second case study, cloud services are analyzed that are offered by a large IT service provider. A generalization of case studies forms the basis for the requirement specifications which are used for state-of-the-art analysis. Although, some solutions for particular challenges have already been provided, a scalable approach for performance problem diagnosis and prediction is still missing. For addressing this issue, a distributed scalable monitoring and analytics framework is presented in the first part of this thesis. We conducted a thorough analysis of technologies to be used by our framework. The framework makes use of existing monitoring and analytics technologies. However, we develop custom collectors to retrieve data non-intrusively from different layers of cloud. In addition, we develop the analytics subscriber and publisher components to retrieve service related events from different APIs and sends alerts to the SLA Management component for taking corrective measures. Further, we implemented an Open Cloud Computing Interface (OCCI) monitoring extension using OCCI Mixin mechanism. To deal with performance problem diagnosis, a novel distributed parallel approach for performance anomaly detection is presented. First all anomalous metrics are found from a distributed database of time-series for a particular window. For comparative analysis three light-weight statistical anomaly detection techniques are selected. We extend these techniques to work with MapReduce paradigm and assess and compare the methods in terms of precision, recall, execution time, speedup and scale up. Next, we correlate the anomalous metrics with the target SLO in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies. Finally, we present the design and implementation of an online anomaly prediction system for cloud computing infrastructures. We further present an experimental evaluation of a set of anomaly prediction methods that aim at predicting upcoming periods of high utilization or poor performance with enough lead time to enable the appropriate scheduling, scaling, and migration of virtual resources. Using real data sets gathered from Cloud platforms of a university data center, we compare several approaches ranging from time-series (e.g. auto regression (AR)) to statistical classification methods (e.g. Bayesian classifier). We observe that linear time-series models, especially AR models, are most likely suitable to model QoS measures and forecast their future values. Moreover, linear time-series models can be integrated with Machine Learning (ML) methods to improve proactive QoS management.

With the advent of cloud computing based technologies, the way to store and manage data has changed completely. With more data being generated every day, especially with other associated paradigms like Internet of Things (IoT), there will be a lot of data to be stored and managed in the cloud. Thus, it will be very important to know which cloud service is more suitable for what kind of data and data-management tasks. To that end, this paper proposes and implements a cloud storage and collaboration tool called OmniBox. The first goal of Omnibox is to provide an evaluation of different cloud providers. The tool monitors different parameters and provides statistics such as upload throughput, download throughput, jitter, single-key and multi-key user accounts, and concurrent download time. The second goal of Omnibox is to provide a unified data management service where users can manage files from different cloud providers within a single interface. Additionally, OmniBox provides a feature called smart upload, where cloud providers are evaluated and suitable service is selected for uploading a file based on Quality of Service (QoS), file-type, file-size, upload throughput, download throughput, available space, jitter, and latency. Our current implementation includes Dropbox and Box Application Programming Interfaces (APIs).

Modern technological advances have given rise to the popularity and success of cloud in organizations and industries because it provides data storage facility and computing services at minimal cost. Data security is one of the important requirements for outsourced data in a cloud storage environment. It also brings new security vulnerabilities for assuring the data confidentiality, data integrity and data access control. Data security of a cloud system depends heavily on the cloud service provider, data owner and cloud users. So, there needs to be some methodology to monitor security vulnerabilities and providing data security in cloud environment. Now a day’s providing data security in cloud storage is one of the most critical tasks. For the past few years many works have been done to assure data security but they are failed in providing data security such as information leakage, secret-key management, cloud-user authentication, breach of data confidentiality, because of collusion attack and lots of computational activities (because of large number of secret keys).In this paper, we used threshold cryptography technique for providing data security in cloud storage system. Proposed approach addresses the data security issues in cloud system more efficiently. In this Threshold cryptography technique data owner create users in groups based on place or location, project, and department, data owner allocates single secret key to each group of users for decryption of data and every users in the group shares parts or pieces of the secret key. Proposed methodology also uses data access control list to control the data access. Proposed approach provides data security more efficiently, it also increases the performance of the system and also it minimizes the number of secret keys.

Cloud storage provides great benefits and advantages like reduction in cost, increased flexibility, elasticity and the efficient use of resources. With these great advantages, there are concerns and problem for cloud users. Some obstacles in using cloud computing services are because of its distributed and untransparent nature. That's why users have no control over their data and information and are not sure of whether or not to trust cloud providers. Therefore, the objective of this research is to examine the influence of customer and provider characteristics on cloud service outcome, impact of Trust on cloud service outcome, and to examine if Trust mediate the relationship of customer characteristics and provider’s characteristics with cloud service outcome. In order to achieve the objectives of this research, the analyses of measurement model, factor analysis, assessment of structure model and regression analysis has been conducted. The Findings of this study shows that the existing threats in cloud service is one of the reason why users are not trusting cloud service providers. These threats which are privacy and security cause unwillingness to use cloud services. This study also found that Trust plays an enormous role for cloud service users and providers because it increases usage, willingness to use among customers, and reputation of cloud service providers. It also found that Outcome is really low if there is no trust in these services. This study will help cloud service providers/developers to know the reasons why some people and organizations are not willing to use their services.

With the development and application of cloud computing, cloud storage is becoming more and more popular with many organizations, but traditional principal-minor structure for cloud storage is at risk of single point of failure. Moreover, it has brought safety risk. Cloud storage security has become the key problem of restricting the development of cloud computing. The paper has a deep research of cloud storage structure and cloud storage security, proposes a entity authentication protocol for P2P cloud storage based on ternary Huffman Merkle hash tree (HuffMHT).This method using the concept of ternary HuffMHT can obtain an effective safe strategy. At the same time, symmetrical key algorithm and public key algorithm are just combined to reduce the authentication delay effectively and increase the network lifetime and enhances the security of the networks. Moreover, signatures of knowledge is used in this paper, which notes number of hash is reduced, the power which notes consume is debased, the network load is decreased. The performance of the system is discussed, and the encryption method has high efficiency and high security.

In cloud storage the data clients can remotely store their data and use on-demand high- quality applications. Data outsourcing users are reassured from the trouble of data storage and maintenance when users put their data in huge size on the cloud, the data integrity protection is a challenging one which enabling public audit for cloud data storage security. Users can ask an external inspection of third party to check the integrity of the out sourced data. However, in such a scheme, the malicious cloud might still forge valid authenticators later than the key-revelation time period if it obtains the current secret key of Data Client. In this paper, propose a prototype named Robust Key Revelation of public auditing for secure cloud storage, in which the security of cloud storage auditing not only earlier than but also later than the key revelation can be preserved. Formalized the definition and the security model of this new kind of cloud storage auditing and design a concrete scheme. In this proposed prototype, the key revelation in one-time period doesn’t affect the security of cloud storage auditing in other time periods. The accurate security proof and the experimental results demonstrate that this proposed prototype achieves desirable security and efficiency with the time intervals. Keywords: Secure Cloud Storage, Robust Key Revelation, Security, Data Client, and Public Auditing.