Local Data Storage Research Articles

Efficient Public Verification of Data Integrity for Cloud Storage Systems from Indistinguishability Obfuscation

Cloud storage services allow users to outsource their data to cloud servers to save local data storage costs. However, unlike using local storage devices, users do not physically manage the data stored on cloud servers; therefore, the data integrity of the outsourced data has become an issue. Many public verification schemes have been proposed to enable a third-party auditor to verify the data integrity for users. These schemes make an impractical assumption—the auditors have enough computation capability to bear expensive verification costs. In this paper, we propose a novel public verification scheme for the cloud storage using indistinguishability obfuscation, which requires a lightweight computation on the auditor and the delegate most computation to the cloud. We further extend our scheme to support batch verification and data dynamic operations, where multiple verification tasks from different users can be performed efficiently by the auditor and the cloud-stored data can be updated dynamically. Compared with other existing works, our scheme significantly reduces the auditor’s computation overhead. Moreover, the batch verification overhead on the auditor side in our scheme is independent of the number of verification tasks. Our scheme could be practical in a scenario, where the data integrity verifications are executed frequently, and the number of verification tasks (i.e., the number of users) is numerous; even if the auditor is equipped with a low-power device, it can verify the data integrity efficiently. We prove the security of our scheme under the strongest security model proposed by Shi et al. (ACM CCS 2013). Finally, we conduct a performance analysis to demonstrate that our scheme is more efficient than other existing works in terms of the auditor’s communication and computation efficiency.

New Hybrid Approach for Secure Data Storage in Cloud Computing Environment

> Cloud storages in cloud data centers are used for enterprises and individuals to store and access their data remotely anywhere anytime without any additional burden. By data outsourcing, users can relieve the burden of local data storage and maintenance. However, one of the major problems of cloud environment is data storage security. Therefore, data auditing is introduced to verify the data in the cloud storages with the aid of Third Party Auditor (TPA). However, the TPA is leased by cloud service provider and after a time the provider may contract with the TPA to conceal the loss of data from the user to prevent the defamation. This paper presents a new hybrid approach for data storage security in cloud computing to protect organizations’ data from the cloud provider, the third party auditor and some users who may use their old accounts to access the data stored on the cloud. The hybrid approach enhances the authentication level of security by combining two efficient authentication techniques; Time-based One Time Password (TOTP) for cloud users verification and Automatic Blocker Protocol (ABP) to fully protect the system from unauthorized TPA. The experimental results demonstrate the effectiveness and efficiency of the proposed approach when auditing shared data integrity.

A Proposal for Standardized Data Attribution and Ownership in the Cloud Environment

Since the earliest days of cloud computing there has been a steady migration of data from local data stores to the cloud. As more and more cloud platforms become available, this inflow of data has only increased dramatically. By some estimates, "the cloud" will hold 50% of all data by 2020. Localized management of data is typically handled using tools, policies and access control methods that are appropriate to the local environment in question. Once data has been migrated to the cloud, these local tools and policies are rarely applicable and a new approach must be taken. Other works have focused on the problems associated with cloud security. This paper try to determine attribution and ownership for data in the cloud. Just because data is stored in account X that does not necessarily mean that X owns all the data in that account. An approach based on the use of a Public Key Infrastructure (PKI) is addressed to provide cryptographically strong data attribution and attestation for data in the cloud.

Factors to be considered in cloud computing adoption

Technology plays an important role in helping organizations control quality and costs, and take advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to confirm the factors to be considered for cloud computing adoption in Australian regional municipal governments. The research involved data from interviews with IT managers from selected regional municipal governments, and survey data from 480 IT staff across 47 regional municipal governments. The major factors to be considered for the adoption of cloud computing in regional municipal governments were identified as Internet connectivity, Internet speed, availability, reliability, data storage location, security, data sovereignty, cost, integration, data backup, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when regional municipal governments planning to adopt cloud computing.

DOAS: Efficient data owner authorized search over encrypted cloud data

Data outsourcing service can shift the local data storage and maintenance to cloud service provider (CSP) to ease the burden from data owner, but it brings the data security threats as CSP is always considered to honest-but-curious. Therefore, searchable encryption (SE) technique which allows cloud clients (including data owner and data user) to securely search over ciphertext through keywords and selectively retrieve files of interest is of prime importance. However, in practice, data user’s access permission always dynamically varies with data owner’s preferences. Moreover, existing SE schemes which are based on attribute-based encryption (ABE) incur heavy computational burden through attribution revocation and policy updating. To allow data owner to flexibly grant access permissions, we design a secure cryptographic primitive called as efficient data owner authorized search over encrypted data scheme through utilizing identity-based encryption (IBE) technique. The formal security analysis proves that our scheme is secure against chosen-plaintext attack (CPA) and chosen-keyword attack (CKA) without random oracle. Besides, empirical experiments over real-world dataset show that our scheme is efficient and feasible with regard to data access control.

Cryptographic Public Verification of Data Integrity for Cloud Storage Systems

Cloud storage services allow users to outsource their data to cloud servers to save on local data storage costs. However, unlike using local storage devices, users don't physically own the data stored on cloud servers and can't be certain about the integrity of the cloud-stored data. Many public verification schemes have been proposed to allow a third-party auditor to verify the integrity of outsourced data. However, most of these schemes assume that the auditors are honest and reliable, so are vulnerable to malicious auditors. Moreover, in most of these schemes, an external adversary could modify the outsourced data and tamper with the interaction messages between the cloud server and the auditor, thus invalidating the outsourced data integrity verification. This article proposes an efficient and secure public verification of data integrity scheme that protects against external adversaries and malicious auditors. The proposed scheme adopts a random masking technique to protect against external adversaries, and requires users to audit auditors' behaviors to prevent malicious auditors from fabricating verification results. It uses Bitcoin to construct unbiased challenge messages to thwart collusion between malicious auditors and cloud servers. A performance analysis demonstrates that the proposed scheme is efficient in terms of the user's auditing overhead.

Privacy-Preserving and Regular Language Search Over Encrypted Cloud Data

Using cloud-based storage service, users can remotely store their data to clouds but also enjoy the high quality data retrieval services, without the tedious and cumbersome local data storage and maintenance. However, the sole storage service cannot satisfy all desirable requirements of users. Over the last decade, privacy-preserving search over encrypted cloud data has been a meaningful and practical research topic for outsourced data security. The fact of remote cloud storage service that users cannot have full physical possession of their data makes the privacy data search a formidable mission. A naive solution is to delegate a trusted party to access the stored data and fulfill a search task. This, nevertheless, does not scale well in practice as the fully data access may easily yield harm for user privacy. To securely introduce an effective solution, we should guarantee the privacy of search contents, i.e., what a user wants to search, and return results, i.e., what a server returns to the user. Furthermore, we also need to guarantee privacy for the outsourced data, and bring no additional local search burden to user. In this paper, we design a novel privacy-preserving functional encryption-based search mechanism over encrypted cloud data. A major advantage of our new primitive compared with the existing public key based search systems is that it supports an extreme expressive search mode, regular language search. Our security and performance analysis show that the proposed system is provably secure and more efficient than some searchable systems with high expressiveness.

VMKDO: Verifiable multi-keyword search over encrypted cloud data for dynamic data-owner

The advantages of cloud computing encourage individuals and enterprises to outsource their local data storage and computation to cloud server, however, data security and privacy concerns seriously hinder the practicability of cloud storage. Although searchable encryption (SE) technique enables cloud server to provide fundamental encrypted data retrieval services for data-owners, equipping with a result verification mechanism is still of prime importance in practice as semi-trusted cloud server may return incorrect search results. Besides, single keyword search inevitably incurs many irrelevant results which result in waste of bandwidth and computation resources. In this paper, we are among the first to tackle the problems of data-owner updating and result verification simultaneously. To this end, we devise an efficient cryptographic primitive called as verifiable multi-keyword search over encrypted cloud data for dynamic data-owner scheme to protect both data confidentiality and integrity. Rigorous security analysis proves that our scheme is secure against keyword guessing attack (KGA) in standard model. As a further contribution, the empirical experiments over real-world dataset show that our scheme is efficient and feasible in practical applications.

Smart IoT Gateway For Heterogeneous Devices Interoperability

The Internet of things (IoT) will interconnect a huge amount of devices, leading to a new way of interaction in the physical and virtual world, inspired by the idea of ubiquity, where all the objects around us, such as: sensors, automobiles, refrigerators, thermostats, industrial robots, tablets, smartphones, etc. could be connected anytime and anywhere. However, one of the main challenges that faces IoT is the high degree of heterogeneity in terms of communication capabilities of the devices, protocols, technologies or hardware. We focus on the implementation of a new Smart IoT Gateway designed to allow interconnection and interoperability between heterogeneous devices in the IoT. The proposed gateway offers significant advantages: (i) it enables connectivity of different protocols and traditional communication technologies (Ethernet) and wireless (ZigBee, Bluetooth, Wi-Fi); (ii) it uses a flexible protocol that translates all the data obtained from the different sensors into a uniform format, performing the analysis of the data obtained from the environment-based-rules related to the different types of sensors; (iii) it uses a lightweight and optimal protocol on the use of devices with limited resources for delivering information environment; and (iv) it provides local data storage for later use and analysis. Our proof of concept demonstrates the performance and capacity of the proposed Smart IoT Gateway is related with Active and Healthy Aging (AHA).

Abstract 3166: Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART

Abstract All translational research projects basically share the same design, which is correlating variation in disease phenotype to variation in underlying biology. Typical questions to be addressed are: ‘Which (out of thousands) biomarkers predict good/bad outcome?’ and ‘Which (out of thousands) biomarkers predict whether a patient will benefit from a particular therapy?’. In line with this concept, many research teams have generated large amounts of experimental molecular data from patient samples, yet generally this information is inaccessible for examination due to local storage of both (meta)data and the data processing workflows used. Alternatively, data stored in central databases may only be available for exploration and interpretation by data specialists, provided that the processing workflow has been published and is available. Thus, if data is not recorded and easily retrievable, validation of obtained results (e.g. promising biomarkers) may be virtually impossible. Additionally, it will be difficult to query existing data sets to answer new questions, which may lead to experiments being unnecessarily repeated and biological materials being wasted. In the Netherlands, the Translational Research IT (TraIT) project initiated by the Center for Translational Molecular Medicine (CTMM, www.ctmm-trait.nl) aims to provide IT solutions to support translational research from start to end, including sustainable management and analysis of data. These data types involve the clinical, biomedical imaging, biobanking, (molecular) experimental data domains and their associated workflows. In addition to domain-specific solutions to manage these data types, the processed or ‘final’ data of these different domains will become available in the data-integration platform tranSMART for querying, visualization and analysis. To ensure sustainable data stewardship and provide easy access to existing data for the CTMM colorectal cancer project ‘Decrease in Colorectal Cancer Death (DeCoDe)’, data has been made available in tranSMART and more datasets are being added. The data encompasses both clinical and (molecular) experimental data from non-high-throughput molecular profiling (NHTMP) assays and array-based profiling techniques. It is now possible with tranSMART to explore the respective DeCoDe datasets and perform various analyses (survival, Fisher-exact tests, ANOVA, aCGH tests etc.), without needing deep bioinformatics expertise. Through metadata tags it is possible to trace back raw or pre-processed data in other tools (e.g. clinical data in OpenClinica, array-data in GEO, or histological images of tissue microarrays). Data can be examined in more detail by domain experts using tools they are familiar with or other tools provided by CTMM-TraIT. Thus, existing rich data are made findable, accessible, interoperable and reusable (FAIR) and source data can be traced back for customized processing. Citation Format: Mariska Bierkens, Wim van der Linden, Ward Weistra, Kees van Bochove, Jeroen A.M. Beliën, Remond J.A. Fijneman, Rita Azevedo, Jan-Willem Boiten, Gerrit A. Meijer. Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3166.

Data catalog project—A browsable, searchable, metadata system

Modern experiments are typically conducted by large, extended groups, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements that can be difficult for users to find, access and understand. We are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements.The data catalog project is a data dictionary and index which enables users to record general descriptive metadata, use cases and rendering information as well as providing them a transparent data access mechanism (URI). Users describe their diagnostic including references, text descriptions, units, labels, example data instances, author contact information and data access URIs. The list of possible attribute labels is extensible, but limiting the vocabulary of names increases the utility of the system. The data catalog is focused on the data products and complements process-based systems like the Metadata Ontology Provenance project [Greenwald, 2012; Schissel, 2015].This system can be coupled with MDSplus to provide a simple platform for data driven display and analysis programs. Sites which use MDSplus can describe tree branches, and if desired create ‘processed data trees’ with homogeneous node structures for measurements. Sites not currently using MDSplus can either use the database to reference local data stores, or construct an MDSplus tree whose leaves reference the local data store.A data catalog system can provide a useful roadmap of data acquired from experiments or simulations making it easier for researchers to find and access important data and understand the meaning of the data and how it was obtained. This is particularly useful in research facilities that study the results of many different experiments or simulations and may not know the intricacies of the data organization in use where the data was generated. It is also possible to store a local copy of key data items in local MDSplus trees and then add processed data to the local catalog.

Could the Recently Enacted Data Localization Requirements in Russia Backfire?

In the wake of ex-NSA contractor Edward Snowden’s surveillance revelations to the global public, and despite an unimpeded cross-border data flow and knowledge transfer being a prerequisite for the development of the digital economy, governments around the world are increasingly tending to resort to mandatory local data storage and to restrictions and bans on data transfers in a rather drastic attempt to protect their citizens’ data security and privacy. This article discusses the legal and economic aspects of the recently enacted data localization requirements in the Russian Federation, as well as the consequences for costs, uncertainty, and the hampering of business activities that may arise if they are applied unaltered. The principal conclusion reached is that a less extreme solution that adheres to or builds upon the existing global practices and takes into account non-economic goals should be considered.

Universal Artifacts

In most BPM systems (a.k.a. workflow systems), the data for process execution is scattered across databases for enterprise, auxiliary local data stores within the BPM systems, and even file systems (e.g., specification of process models). The interleaving nature of data management and BP execution and the lack of a coherent conceptual data model for all data needed for execution make it hard for (1) providing Business-Process-as-a-Service (BPaaS) and (2) effective support for collaboration between business processes. The primary reason is that an enormous effort is required for maintaining both the engines and the data for the client applications. In particular, different modeling languages and different BPM systems make process interoperation one of the toughest challenges. In this article, we formulate a concept of a “universal artifact,” which extends artifact-centric models by capturing all needed data for a process instance throughout its execution. A framework called SeGA based on universal artifacts is developed to support separation of data and BP execution, a key principle for BPM systems. We demonstrate in this article that SeGA is versatile enough to fully facilitate not only executions of individual processes (to support BPaaS) but also various collaboration models. Moreover, SeGA reduces the complexity in runtime management including runtime querying, constraints enforcement, and dynamic modification upon collaboration across possibly different BPM systems.

Overview of current techniques in remote data auditing

Abstract The emergence of cloud computing brings the infinite imagination space, both in individual and organizations, due to its unprecedented advantages in the IT history: on-demand self-service, ubiquitous network access, location independent resource pooling, rapid resource elasticity, usage-based pricing and transference of risk. Many of the individuals or organizations ease the pressure on their local data storage, and mitigate the maintenance overhead of local data storage by using outsource data to cloud. However, the data outsourcing is not absolutely safe in the cloud. In order to enhance the users’ confidence of the integrity of their outsource data in the cloud. To promote the rapid deployment of cloud data storage service and regain security assurances with outsourced data dependability, many scholars tend to design the Remote Data Auditing (RDA) technique as a new concept to enable public auditability for the outsourced data in the cloud. The RDA is a useful technique to ensure the correctness of the data outsourced to cloud servers. This paper presents a comprehensive survey on techniques of remote data auditing in cloud server. Recently, more and more remote auditing approaches are categorized into the three different classes, that is, replication-based, erasure coding-based, and network coding-based to present a taxonomy. This paper also aims to the explore major issues.

Prefetching-Based Data Dissemination in Vehicular Cloud Systems

In the last decade, vehicular ad hoc networks (VANETs) have been widely studied as an effective method for providing wireless communication connectivity in vehicular transportation systems. In particular, vehicular cloud systems (VCSs) have received abundant interest for the ability to offer a variety of vehicle information services. We consider the data dissemination problem of providing reliable data delivery services from a cloud data center to vehicles through roadside wireless access points (APs) with local data storage. Due to intermittent wireless connectivity and the limited data storage size of roadside wireless APs, the question of how to use the limited resources of the wireless APs is one of the most pressing issues affecting data dissemination efficiency in VCSs. In this paper, we devise a vehicle route-based data prefetching scheme, which maximizes data dissemination success probability in an average sense when the size of local data storage is limited and wireless connectivity is stochastically unknown. We propose a greedy algorithm and an online learning algorithm for deterministic and stochastic cases, respectively, to decide how to prefetch a set of data of interest from a data center to roadside wireless APs. Experiment results indicate that the proposed algorithms can achieve efficient data dissemination in a variety of vehicular scenarios.

Enabling identity-based cloud storage public auditing with quantum computers resistance

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.

An investigation of the main factors to be considered in cloud computing adoption in Australian regional local councils

Technology plays an important role in helping organisations control quality and costs, and taking advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres through the web. This research aims to investigate the factors to be considered for cloud computing adoption by Australian regional councils. The research employed in-depth interviews with IT managers from selected local government councils. The major factors to be considered for the adoption of cloud computing were identified as Internet connectivity, data storage location, cost, integration, data back-up, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when planning to adopt cloud computing.

An investigation of the challenges and issues influencing the adoption of cloud computing in Australian regional municipal governments

New developments in information technology (IT) provide opportunities for a better quality of life through benefits such as increased comfort and convenience. Compared to dedicated infrastructures such as cluster and grid computing, cloud computing can better cater to users' needs by increasing effectiveness, efficiency and functionality at a potentially lower cost. This research aims to provide insights into the challenges and issues faced by implementers and users of cloud computing by comparing the extant literature about this issue with current insights provided by IT managers. A systematic literature review and in-depth interviews with IT managers in local government councils were conducted for this research. The research indicated that the factors in the extant literature were supported; additional challenges and issues emerged which are related to effective network, data storage location, availability of different service providers, policy makers, a limited understanding of the cloud and business transformation. The findings of this research are expected to assist managers to evaluate possible adoption and increase their awareness about challenges and issues that influence the cloud adoption when planning to adopt it.

Exploiting volatile opportunistic computing resources with Lobster

Analysis of high energy physics experiments using the Compact Muon Solenoid (CMS) at the Large Hadron Collider (LHC) can be limited by availability of computing resources. As a joint effort involving computer scientists and CMS physicists at Notre Dame, we have developed an opportunistic workflow management tool, Lobster, to harvest available cycles from university campus computing pools. Lobster consists of a management server, file server, and worker processes which can be submitted to any available computing resource without requiring root access.Lobster makes use of the Work Queue system to perform task management, while the CMS specific software environment is provided via CVMFS and Parrot. Data is handled via Chirp and Hadoop for local data storage and XrootD for access to the CMS wide-area data federation. An extensive set of monitoring and diagnostic tools have been developed to facilitate system optimisation. We have tested Lobster using the 20 000-core cluster at Notre Dame, achieving approximately 8-10k tasks running simultaneously, sustaining approximately 9 Gbit/s of input data and 340 Mbit/s of output data.

CloudMdsQL: querying heterogeneous cloud data stores with a common language

The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store's native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.