Remote Storage Resources Research Articles

Efficient Continuous Big Data Integrity Checking for Decentralized Storage

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

IntegrityChain: Provable Data Possession for Decentralized Storage

Outsourced storage enables data owners to host their data on remote storage resources without keeping a local copy so as to target their core business. However, a serious problem is data integrity in the sense that data owners lose their physical control over the remote-stored data. Existing provable data possession protocols are overwhelmingly designed for centralized storage such as cloud, in which the server is assumed dishonest but the client is reliable. Moreover, the centralized storage suffers single-point-of-failure threat. In this paper, to deal with these issues, we propose the notion of IntegrityChain, a decentralized storage framework supporting provable data possession (PDP) based on blockchain. We formalize the system model, in which a data owner can store files to the peers in a blockchain network and check the integrity of the outsourced data periodically by paying some cryptocurrencies while the hosts can earn money if honestly provide storage service and will be punished by losing the pre-made deposit if data loss happens. In the security model, we consider the fairness in trading between a host and a data user and the soundness of the underlying decentralized PDP in this system. We come up with a concrete construction by borrowing the idea of multi-replica PDP and proof-of-retrievability and present the security analysis of the proposal. The evaluation for the construction contain two segments: the offchain part, in which we implement the algorithms locally to test the time consumption, and onchain part, in which we program a smart contract and launch it in a test network to test the gas cost for the functions.

Trusting third-party storage providers for holding personal information. A context-based approach to protect identity-related data in untrusted domains

The never ending growth of digital information and the availability of low-cost storage facilities and networks capacity is leading users towards moving their data to remote storage resources. Since users’ data often holds identity-related information, several privacy issues arise when data can be stored in untrusted domains. In addition digital identity management is becoming extremely complicated due to the identity replicas proliferation necessary to get authentication in different domains. GMail and Amazon Web Services, for instance, are two examples of online services adopted by million of users throughout the world which hold huge amounts of sensitive users data. State-of-the-art encryption tools for large-scale distributed infrastructures allow users to encrypt content locally before storing it on a remote untrusted repository. This approach can experience performance drawbacks, when very large data-sets must be encrypted/decrypted on a single machine. The proposed approach extends the existing solutions by providing two additional features: (1) the encryption can also be delegated to a pool of remote trusted computing resources, and (2) the definition of the encryption context which drives the tool to select the best strategy to process the data. The performance benchmarks are based on the results of tests carried out both on a local workstation and on the Grid INFN Laboratory for Dissemination Activities (GILDA) testbed.

Logistical quality of service in NetSolve

From its inception a principal goal of the Next Generation Internet (NGI) has been to find a way to provide reliable, scalable, cost effective, and deployable delivery of data with quality of service (QoS) guarantees as a foundation for innovative NGI applications. But while schemes to provide reliable end-to-end QoS are being actively pursued on a number of fronts, the well known problems with the scalability, cost, and deployability of end-to-end QoS continue to obstruct progress toward achieving this end. Our research focuses on the nature and potential value of an approach to providing QoS that builds on a strategy for allowing NGI applications to dynamically manage remote storage resources in order to stage data locally for later delivery. We call this strategy logistical Quality of Service ( logistical QoS). The concept of logistical QoS is a generalization of the typical end-to-end model for reserving QoS, permitting much more flexible use of buffering of messages in order to achieve QoS delivery without difficult end-to-end requirements. Whenever data is available to be sent well before it needs to be received, it can be staged, i.e. moved in advance to a location close to the receiver for later delivery. Isolating the act of buffering data as a distinct operation, independent of delivery to the receiver, opens up a new dimension of freedom in the management of communication and storage resources that can offer NGI application developers a wide variety of new opportunities to innovate. Our project focuses on the development of logistical QoS as enabling network functionality for application-driven staging and scheduling of distributed computation on NGI. It is divided into two parts: (1) research on the basic network functionality that is required to support logistical QoS that is reliable, scalable, cost effective, and easy to use; and (2) research that investigates the integration of logistical QoS and the basic network technology that underlies it with the scheduling of distributed computations using NetSolve and the Network Weather Service. Our work on logistical QoS focuses on the Internet Backplane, as providing a mechanism for managing remote storage resources, and the Internet Backplane Protocol, as enabling technology for using that mechanism. The idea underlying the concept of the Internet Backplane is that NGI will enable us to consider the global network as an extension of the processor backplane, if only we have a low overhead mechanism for fine grained naming and access to data, analogous to physical addresses and bus transfers. By this analogy the Internet Backplane is a common namespace for fine-grained management of distributed resources. The IBP provides a flexible interface to enable this functionality, allowing reliable and flexible control of remote storage buffers through a general scheme for naming, staging, delivering and protecting data. We will test logistical QoS as an enabling technology for NGI computing using NetSolve. NetSolve is a software environment for networked computing designed to transform disparate computers and software components into a unified, easy-to-access computational service; it is being used by NSF's Partnerships for Advanced Computational Infrastructure to build high-performance systems for distributed computation on leading edge networks. We will investigate the implementation of logistical QoS within NetSolve and the integration of IBP with the Network Weather Service (used to monitor and forecast the performance of network and computational resources) to build a scheduling capability that maximizes the performance of NetSolve across next generation networks.