Provable Data Possession Model Research Articles

Modeling an efficient authentic provable data possession model using legacy filter model for IOT and cloud environment

ABSTRACT Internet of Things (IoT) is extensively adopted to manage everyday life. Data is gathered from IoT devices cloud computing is inevitable to store and analyze the data. The storage over the cloud is also not owned by the end-user. Thus, it is not so feasible. Therefore, two diverse issues are directly connected with the verification of data integrity, i.e. the incoming data should be verified and the verification process is performed. Various prevailing approaches are used for performing data integrity verification over the trusted party and nodes with proper resources. Moreover, it is highly complex to apply different prevalent research methods to IoT devices with constrained resources. This work concentrates on performing secure cloud-based storage over an IoT environment using authentic provable data possession (APDP) using the Legacy filtering model (APDP-LFM). The major contribution is the data possession and filter process to reduce the computational complexity. The experimentation is performed using a MATLAB environment, and the outcomes demonstrate that the proposed model preserves computational time and no complexity over the verification process. This model helps avoid False Positives and efficiently works for the enormous amount of incoming data over the IoT environment.

Blockchain-Based Dynamic Provable Data Possession for Smart Cities

Smart cities have experienced rapid development with the advances of information and communication technologies such as Internet of Things (IoT). To tackle the data storage issues raised by large-scale data generated by IoT devices, an increasing number of enterprises and individuals prefer to outsource their data to cloud, where data integrity becomes a concern to cloud users. A variety of provable data possession (PDP) protocols have been proposed for centralized cloud storage scenarios so far. However, a centralized cloud relies too much on the trust of the central servers and, thus, is prone to the single point of failure. In this article, we describe a blockchain-based PDP model to realize the decentralized outsourcing storage framework, and then present a concrete construction of decentralized PDP by using multireplica storage tricks. Moreover, our protocol provides dynamic operations for outsourced data and at the same time, guarantees the fairness of all parties involved. We provide a detailed security proof for the proposed protocol and deploy a smart contract for the protocols as well. We finally evaluate the algorithms and the implementation results demonstrate the practicability of the proposed protocol.

Secure Cloud Storage Service Using Bloom Filters for the Internet of Things

Today, the Internet of Things (IoT) is used for convenience in everyday life in many areas. Owing to the fact that the data collected from the IoT are generated in large quantities, cloud computing is inevitably used to store and analyze the data. However, cloud storage is not owned by the user, so it is unreliable. Verifying the integrity of data collected in an IoT environment and stored in a cloud has two problems: a large amount of data needs to be verified, and the data verification should be done directly on the IoT device. Many methods for data integrity verification use trusted third parties and devices that provide sufficient resources. However, it is difficult to directly apply existing research to the IoT devices that have limited resources. This paper proposes a secure cloud storage service for an IoT environment that is based on a provable data possession model and uses Bloom filters. The experimental results showed that the proposed method saves time and has no significant differences in the verification rate with existing methods, even though the Bloom filter causes false positives. Therefore, the proposed service can effectively process a large amount of data generated in an IoT environment.

Blockchain-Based Private Provable Data Possession

Remote data secure storage is of crucial importance in cloud computing. In order to check remote data integrity, an important paradigm PDP (i.e., provable data possession) is proposed. All the existing PDP schemes make use of RSA or bilinear pairings. One large file has to be divided into a great many of blocks. For example, 1T (Terabit) file has to be divided into $1.0737 \times 10^9$ 1 . 0737 × 10 9 blocks (RSA where the length of the index is 1024 bits) or $6.8719 \times 10^9$ 6 . 8719 × 10 9 blocks (bilinear pairings where the order of the elliptic curve is 160 bits). The huge computation cost and communication cost incurs the inefficient PDP implementation. In other words, they are not practical. In order to solve the problem, we propose a new PDP model: blockchain-based private PDP. The new concept makes use of blockchain which is the core of cryptocurrency. For the new concept, the paper formalizes its system model and security model. Then, a concrete blockchain-based private PDP scheme is designed by making use of blockchain and RSA. The proposed blockchain-based private PDP scheme is provably secure. At the same time, we also analyze its performance from two parts: theory analysis and implementation prototype. Our analysis shows that the proposed PDP scheme is secure, efficient and practical.

Cloud Storage Auditing by Utilizing Provable Data Possession Method

Cloud storage permits users to remotely store their knowledge and revel in the on-demand top quality cloud applications while not the burden of native hardware and software package management. Though the advantages are clear, such a service is addition relinquishing users’ physical possession of their outsourced knowledge, which necessarily poses new security risks towards the correctness of the knowledge in cloud. In order to deal with this new drawback and additional succeed a secure and dependable cloud storage service, we tend to propose during this paper we introduce a model for provable data possession (PDP) that allows a client that has Stored data at an untrusted server to verify that the server possesses the original data without capturing it.The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs and also propose a versatile distributed storage integrity auditing mechanism, implementing the homomorphic token and distributed erasure-coded information. The planned style permits users to audit the cloud storage with terribly light-weight communication and computation value. The auditing result not solely ensures robust cloud storage correctness guarantee, but at the same time at identical time achieves fast information error localization, i.e., the recognition of misbehaving server. Considering the cloud information are energetic in nature, the proposed design further supports secure and desirable dynamic operations on outsourced information, including block modification, deletion, and append. Analysis shows the planned theme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and continuous server colluding attacks.

PADP: A parallel data possession audit model for cloud storage

SummaryData integrity is one of the most important attributes need to be concerned by the customers of cloud storage. We propose a parallel audit model for data possession in distributed cloud storage environment. The model utilizes the virtues of both a provable data possession model and a data possession audit model and adopts a third‐party auditor to separate users from storage service providers. By introducing the audit log mechanism, it can significantly reduce the storage and computation overhead of data possession audit while guarantee the safety of audit process. Moreover, according to the characteristics of parallel computing in cloud storage environment, we propose a data possession proving algorithm called MapReduce‐based Parallel Challenge Proof Generation Algorithm (MR_PCPGA) by improving the data possession prove algorithm based on homomorphic tag in provable data possession model. Extensive security analysis and experimental results show that the proposed schemes are provably secure and highly efficient in distributed cloud storage environment. Copyright © 2017 John Wiley & Sons, Ltd.

Online/Offline Provable Data Possession

Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires only lightweight computations like modular multiplications. We present a general OOPDP transformation framework which is applicable to PDP-related schemes with metadata aggregatability and public metadata expansibility . Following the framework, we present two efficient OOPDP instantiations. Technically, we present aggregatable vector Chemeleon hash functions which map a vector of values to a group element and play a central role in the OOPDP transformation. Theoretical and experimental analyses confirm that our technique is practical to speed-up PDP schemes.

Data Integrity Checking Protocol with Data Dynamics in Cloud Computing

We introduce a model for provable data possession (PDP) which allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. In a previous work, Ateniese et al. proposed a remote data integrity checking protocol that supports data partial dynamics. In this paper, we present a new remote data possession checking protocol which allows an unlimited number of file integrity verifications and efficiently supports dynamic operations, such as data modification, deletion, insertion and append. The proposed protocol supports public verifiability. In addition, the proposed protocol does not leak any private information to third-party verifiers. Through a specific analysis, we show the correctness and security of the protocol. After that, we demonstrate the proposed protocol has a good performance.

Dynamic Provable Data Possession

As storage-outsourcing services and resource-sharing networks have become popular, the problem of efficiently proving the integrity of data stored at untrusted servers has received increased attention. In the Provable Data Possession (PDP) model, the client preprocesses the data and then sends them to an untrusted server for storage while keeping a small amount of meta-data. The client later asks the server to prove that the stored data have not been tampered with or deleted (without downloading the actual data). However, existing PDP schemes apply only to static (or append-only) files. We present a definitional framework and efficient constructions for Dynamic Provable Data Possession (DPDP), which extends the PDP model to support provable updates to stored data. We use a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O (1) to O (log n (or O ( n ε log n )) for a file consisting of n blocks while maintaining the same (or better, respectively) probability of misbehavior detection. Our experiments show that this slowdown is very low in practice (e.g., 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g., CVS).

Security Data Auditing based on Multifunction Digital Watermark for Multimedia File in Cloud Storage

Cloud computing is a promising computing model that enables convenient and on-demand network access to a shared pool of configurable computing resources. In cloud computing, data owners host their data on cloud servers and data consumers can access the data from cloud servers. This new paradigm of data storage service also introduces new security challenges, because data owners and data servers have different identities and different business interests. Therefore, an independent auditing service is required to make sure that the data is correctly hosted in the Cloud. However, the existing solutions are not specific to the multimedia data. Moreover copyright protection is not provided. In this paper, we define specially a provable data possession model for multimedia file, and present a framework based on digital watermarking for multimedia data storages audit service, in which we analyze the security features of audit service for multimedia data outsourcing and the corresponding properties of digital watermarking. Moreover as an example, we present a provable data possession scheme based on double function self-embedded digital watermark, which integrate image content audit service and copyright protection.

Mutual Trust to Provide Data Security in Cloud Environment

We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage systems. It is also superior in performance by minimizing the use of expensive public-key cryptography in metadata management. We present the architecture and implementation of various SHAROES com- ponents and our experiments demonstrate performance superior to other proposals by over 40% on a number of benchmarks.