Effective Privacy Preservation in Third-Party Cloud Storage Auditing

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A Comprehensive Survey on Data Integrity Proving Schemes in Cloud Storage

Cloud computing requires broad security solutions based upon many aspects of a large and lightly integrated system. The cloud data storage service releases the users from the burden of huge local data storage and their preservation by out- sourcing mass data to the cloud. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. One of the significant concerns that need to be spoken is to assure the customer of the integrity i.e. rightness of his data in the cloud. The data integrity verification is done by introducing third party auditor (TPA) who has privileges to check the integrity of dynamic data in cloud on behalf of cloud client. Cloud client can get notification from TPA when the data integrity is lost. These systems have sustenance data dynamics via the data operation such as data modification, insertion, deletion. Many work has been done but it lacks the support of either public auditability or active data processes To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: (i) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; (ii) The third party auditing process should bring in no new vulnerabilities towards user data privacy. Here, a proposed scheme is discussed in which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service level agreement (SLA). This scheme ensures that the storage at the client side is minimal which will be beneficial for the organization. In this paper, we define a survey on Cloud computing and provide the architecture for creating C

Toward efficient and secure public auditing for dynamic big data storage on cloud

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud and Big Data are two of the most attractive ICT research topics that have emerged in recent years. Requirements of big data processing are now everywhere, while the pay-as-you-go model of cloud systems is especially cost efficient in terms of processing big data applications. However, there are still concerns that hinder the proliferation of cloud, and data security/privacy is a top concern for data owners wishing to migrate their applications into the cloud environment. Compared to users of conventional systems, cloud users need to surrender the local control of their data to cloud servers. Another challenge for big data is the data dynamism which exists in most big data applications. Due to the frequent updates, efficiency becomes a major issue in data management. As security always brings compromises in efficiency, it is difficult but nonetheless important to investigate how to efficiently address security challenges over dynamic cloud data. Data integrity is an essential aspect of data security. Except for server-side integrity protection mechanisms, verification from a third-party auditor is of equal importance because this enables users to verify the integrity of their data through the auditors at any user-chosen timeslot. This type of verification is also named 'public auditing' of data. Existing public auditing schemes allow the integrity of a dataset stored in cloud to be externally verified without retrieval of the whole original dataset. However, in practice, there are many challenges that hinder the application of such schemes. To name a few of these, first, the server still has to aggregate a proof with the cloud controller from data blocks that are distributedly stored and processed on cloud instances and this means that encryption and transfer of these data within the cloud will become time-consuming. Second, security flaws exist in the current designs. The verification processes are insecure against various attacks and this leads to concerns about deploying these schemes in practice. Third, when the dataset is large, auditing of dynamic data becomes costly in terms of communication and storage. This is especially the case for a large number of small data updates and data updates on multi-replica cloud data storage. In this thesis, the research problem of dynamic public data auditing in cloud is systematically investigated. After analysing the research problems, we systematically address the problems regarding secure and efficient public auditing of dynamic big data in cloud by developing, testing and publishing a series of security schemes and algorithms for secure and efficient public auditing of dynamic big data storage on cloud. Specifically, our work focuses on the following aspects: cloud internal authenticated key exchange, authorisation on third-party auditor, fine-grained update support, index verification, and efficient multi-replica public auditing of dynamic data. To the best of our knowledge, this thesis presents the first series of work to systematically analysis and to address this research problem. Experimental results and analyses show that the solutions that are presented in this thesis are suitable for auditing dynamic big data storage on cloud. Furthermore, our solutions represent significant improvements in cloud efficiency and security

Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data

A Novel Scheme For Preserving Owner Privacy And Verifying Data Integrity Of Shared Data In Public Cloud Storage

With the emergence of cloud Technologies, it is important review the integrity of the information that is saved on the public cloud storage systems. When critical and private information which is very sensitive in nature is saved and shared with many uses, it is very much important to safeguard the details of the data owner from the auditor. It means that the auditor should not be able to get any details about the data owner while he is auditing are reviewing the cloud data. Many schemes were proposed that safeguard the user privacy while incorporating the confirmable information possession technique. However, the issue with these schemes is that they have heavy computational cost and they increase the load on the systems and in turn bring down the efficiency. To address all the above mentioned problems, we propose a novel and unique technique to up all the data owner's privacy while auditing the data. The architecture of our proposed scheme is based on identity supported encryption and hence it overcomes the problem of management of certificates and ensures the relation between the data owner and the uploaded data are not exposed to the auditor by encrypting the data  in the proof generation phase but not in the auditing phase. The encryption is also done at block level to safeguard the data from untrusted Cloud Service Provider. In this manner, our scheme provides maximum security from the Cloud Service Provider and the auditor and hence the privacy and anonymity of data is preserved in this mechanism. Experimental results show that our mechanism is effective, efficient and implementable when compared to to the existing systems

Implementation of Dynamic Virtual Cloud Architecture for Privacy Data Storage

Nowadays rapidly developing technologies, cloud computing offers versatile services. However, cloud computing presents a challenge to secure information sharing. Customers can securely share their data with others and remotely store it in the cloud using cloud storage services. In recent times, cloud storage typically represents as the primary method of external data storage. The primary challenge is safeguarding the cloud-based data against attacks. Over the information network, the growth of private or semi-private information has increased. The search techniques have not been addressed by privacy safeguards. As there is no suitable audit system, the validity of the stored data has become in question. In addition, user authentication presents additional difficulties. Hence in order to solve these issues, Design and implementation of dynamic virtual cloud architecture for privacy data storage is presented. In this approach, third-party audits are presented accompanied a new, regenerative public audit methodology. A distributed KDC (Key Distribution Center) is employed to encrypt the data. Documents can be stored on a private server in plain word form, which compromise the protection of privacy. As a result, system security can be improved to make the documents safer and more effective. The main objective of this Virtual Cloud Architecture is to achieve data confidentiality, as well as authenticity.&nbsp

An extensive research survey on data integrity and deduplication towards privacy in cloud storage

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems

Data Integrity Check using Hash Functions in Cloud environment

The concept of cloud computing is currently being widely adopted by many business and organizations. Cloud Computing offers immense amount of resources, available for end users by employing various flexible paying methods. The opportunity to choose between several cloud providers is referred by complexity of integrated cloud computing solution. Cloud services offer many benefits to the data owner and users, but to take advantage of the benefits of cloud computing and to make the cloud viable as a computing platform, the data and the service hosted in the cloud must be fully secured. This research paper points out how third party auditors can be avoided and proposes a specific solution which involves the customer safeguarding the data integrity by himself in a very simple and efficient way by utilizing existing hash generating algorithm

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches