Access To Big Data Research Articles

Secure and Verifiable Policy Update Outsourcing for Big Data Access Control in the Cloud

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, as the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-based encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method, however, incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also propose policy updating algorithms for different types of access policies. Finally, we propose an efficient and secure method that allows data owner to check whether the cloud server has updated the ciphertexts correctly. The analysis shows that our policy updating outsourcing scheme is correct, complete, secure and efficient.

A Framework for Composition and Enforcement of Privacy-Aware and Context-Driven Authorization Mechanism for Multimedia Big Data

The proliferation of multimedia big data for dissemination and sharing of massive amounts of information raises important security and privacy concerns. One such concern is the composition and enforcement of privacy policies in order to securely manage access of multimedia big data. Several researchers have pointed out that for proper enforcement of privacy policies, the privacy requirements should be captured in access control systems. In this paper, we propose a hybrid approach where privacy requirements are captured in an access control system and present a framework for composition and enforcement of privacy policies. The focus is to allow a user, not a system or security administrator to compose conflict free policies for their online multimedia data. An additional requirement is that such a policy be context-aware. We also present a methodology for verifying the privacy policy in order to ensure correctness and logical consistency. The verification process is also used to ensure that sensitive security requirements are not violated when privacy rules are enforced. A prototype, named Intelligent Privacy Manager (iPM), has been implemented for sharing of multimedia big data in a secure and private manner.

A trustworthy access control model for mobile cloud computing based on reputation and mechanism design

Mobile cloud computing (MCC) is an emerging technology that has gained ever-increasing popularity, which makes the generation and large-scale collection of private personal data possible. However, new security issues arise when MCC offers big data analytics and management services. In particular, there is an absence of fine-grained secure access control model to protect privacy information from unauthorized access, especially launched by internal malicious nodes with legal identity and authority. To fill the gap, this paper proposes a reputation and mechanism design based trustworthy access control model (RMTAC) to provide secure and privacy-aware big data access control in MCC. The RMTAC integrates the access control scheme with Vickrey–Clark–Groves (VCG) based adaptive reputation mechanism (VARM), the distributed multi-level security scheme and the hierarchical key management protocol to provide secure and privacy-aware access control and defend against the internal attacks. Simulation results demonstrate the superior performance of the VARM in terms of utility, effective recommendation rate, and accuracy rate compared to the existing reputation mechanisms. Moreover, the RMTAC shows better performance in terms of success rate of malicious access and successful acceptance rate compared to the role-based encryption access control model (RBE) mechanism, in the presence of collusion attacks, bad mouthing attacks and information disclosure attacks.

GRASP-based resource re-optimization for effective big data access in federated clouds

Federated cloud organizations, spanning across multiple networked sites that provide both computing and storage resources, can be considered the state-of-the-art solutions for providing multi-tenant runtime services in modern distributed processing environments. In these scenarios, by re-optimizing the communication paths between virtual machines and big data sources, at evenly spaced interval or when required by circumstances, the overall communication and runtime resource utilization on the cloud infrastructure is re-balanced, so that more virtual machines can be allowed to access the needed big data sources with adequate bandwidth, thereby significantly improving the perceived performance and quality of service. The problem of re-optimization is tackled with a powerful meta-heuristic, the greedy randomized adaptive search procedure (GRASP), augmented by path re-linking. In order to evaluate the proposed approach, extensive simulations have been performed, leading to very interesting results, demonstrating the effectiveness and validity of the underlying ideas and their applicability to real large-scale federated cloud scenarios.

Person-centred health care: a critical assessment of current and emerging research approaches.

Person-centred health care is prominent in international health care reforms. A shift to understanding and improving personal care at the point of delivery has generated debates about the nature of the person-centred research agenda. This paper purviews research paradigms that influence current person-centred research approaches and traditions that influence knowledge foundations in the field. It presents a synthesis of the emergent approaches and methodologies and highlights gaps between static academic research and the increasing accessibility of evaluation, informatics and big data from health information systems. Paradigms in health services research range from theoretical to atheoretical, including positivist, interpretive, postmodern and pragmatic. Interpretivist (subjective) and positivist (objectivist) paradigms have been historically polarized. Yet, integrative and pragmatic approaches have emerged. Nevertheless, there is a tendency to reductionism, and to reduce personal experiences to metrics in the positivist paradigm. Integrating personalized information into clinical systems is increasingly driven by the pervasive health information technology, which raises many issues about the asymmetry and uncertainty in the flow of information to support personal health journeys. The flux and uncertainty of knowledge between and within paradigmatic or pragmatic approaches highlights the uncertainty and the 'unorder and disorder' in what is known and what it means. Transdisciplinary, complex adaptive systems theory with multi-ontology sense making provides an overarching framework for making sense of the complex dynamics in research progress. A major challenge to current research paradigms is focus on the individualizing of care and enhancing experiences of persons in health settings. There is an urgent need for person-centred research to address this complex process. A transdisciplinary and complex systems approach provides a sense-making framework.

Research on Database Schema Comparison of Relational Databases and Key-Value Stores

With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summarized.

Machine learning for Big Data analytics in plants.

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.

분산환경에서 빅 데이터 처리 기법

소셜 네트워크와 스마트폰의 대중화로 인하여 서버에 저장되어 있는 데이터를 손쉽게 사용할 수 있는 빅데이터 서비스가 증가하고 있다. 빅 데이터 처리기술은 빅 데이터 서비스에서 가장 중요한 기술 중 하나지만 보안에 대한 해결책이 미미한 상태이다. 본 논문에서는 빅 데이터 서비스에서 제공되는 분산된 대용량 데이터를 이중 해쉬를 이용하여 사용자가 손쉽게 데이터에 접근할 수 있는 다중 해쉬 체인 기반의 데이터 분산 처리 기법을 제안한다. 제안 기법은 빅 데이터를 데이터의 종류, 기능, 특성에 따라 해쉬 체인으로 묶어 데이터에 높은 처리량을 지원한다. 또한, 토큰 및 데이터 노드가 공격자에게 노출되었을 때 발생하는 보안 취약점을 해결하기 위해서 데이터의 속성 정보를 해쉬 체인의 연결 정보로 활용하여 빅 데이터의 접근 제어를 분산 처리한다. Social network server due to the popularity of smart phones, and data stored in a big usable access data services are increasing. Big Data Big Data processing technology is one of the most important technologies in the service, but a solution to this minor security state. In this paper, the data services provided by the big -sized data is distributed using a double hash user to easily access to data of multiple distributed hash chain based data processing technique is proposed. The proposed method is a kind of big data data, a function, characteristics of the hash chain tied to a high-throughput data are supported. Further, the token and the data node to an eavesdropper that occurs when the security vulnerability to the data attribute information to the connection information by utilizing hash chain of big data access control in a distributed processing.

NOSQL DATABASES: NEW MILLENNIUM DATABASE FOR BIG DATA, BIG USERS, CLOUD COMPUTING AND ITS SECURITY CHALLENGES

The field of databases has emerged in last decades of years. New architectures try to meet the need to store more and more various kinds of diverse data. The current trend of Big Data(too diverse data, unstructured data, semi-structured data, fast changing data), Big Users(global users 24 hours a day, 365 days a year) and Cloud Computing(new applications use a three-tier internet architecture, run in a public or private cloud) are the driving force for the organizations to migrate towards Non-Relational databases(referring as NoSQL popularly called as “NOT ONLY SQL”)from Relational Databases. The main market of Relational Databases is business data processing and these databases are architected to run a single machine and uses a rigid and schemebased approach to modeling the data and dealing with Big Data and global users on a cloud environment becomes more and more difficult with relational databases. Non-relational databases (NoSQL databases) are considering as new Era database, it provides dynamic schemas, flexible data model, scale-out architecture, efficient big data storage and access requirement. Today the use of NoSQL is mainly due to its Scalability and Performance characteristics. Only a few years ago the Scalability and Performance were not such a big problem but the huge amount of data that is collected today is infinitely much more than ten years ago and also the growth of cloud computing results in large data store even more. This paper includes the introduction, causes of migrating towards NoSQL databases, characteristics, classification of NoSQL databases. Finally the security issues in NoSQL Databases are described and the security enforcement mechanism is proposed.

A User Identification Technique to Access Big Data using Cloud Services

is required in stored database systems so that only authorized users can access the data and related cloud infrastructures. This paper proposes anauthentication technique using multi-factor and multi-dimensional authentication system with multi-level security. The proposed technique is likely to be more robust as the probability of breaking the password is extremely low. This framework uses a multi-modal biometric approach and SMS to enforce additional security measures with the conventional Login/password system. The robustness of the technique is demonstrated mathematically using a statistical analysis. This work presents the authentication system using the consumer authentication architecture diagrams, activity diagrams, data flow diagrams, sequence diagrams, and algorithms.

Big Data 2.0: Cataclysm or Catalyst?

Big Data 2.0: Cataclysm or Catalyst?

Spatial Big Data Organization, Access and Visualization with ESSG

Abstract. There are hundreds of spatial reference frame (SRF) being applied, and the great difference among SRFs has blocked the share of global data on planet Earth. A conceptual spheroid of radius 12,800 km and a spheroid degenerated octree grid method are applied to produce an earth system spatial grid (ESSG), which is of natural characteristics to be applied as a new common SRF. A triple CTA is designed as ESSG-based data structure to organize the big data of planet Earth, and a 2D table of a unique label and limitless records for time slices and attribute values is present to record the data of each grid. The big data on planet Earth can hence be gridded and interrelated without discipline gaps and SRF obstacles. An integral data organization mode is designed, and three potential routes are presented for users to access shareable global data in cloud environment. Furthermore, with global crust, atmosphere, DEM, and satellite image being examples, the integrated visualization of global large objects is demonstrated.

A Data Localization Algorithm for Distributing Column Storage System of Big Data

Distributing column storage is one of the techniques to improve the efficiency of big data access under the cloud computing environment. To achieving the aim and reducing network data access frequency, paper established a data localization strategy and designed a multi-thread algorithm. Firstly, segmentalize data in the horizontal direction, and then divide vertically the data table into data column, and ensure that the same level column data localize on the same node in the cluster. Secondly, the essay designed and realized the data localization algorithm under Hadoop distributed cloud computing framework. Finally, experiments show remarkable reduces in the network access with the usage of data localization algorithm, and improvement of the data access efficiency.

As Personal Genomes Join Big Data Will Privacy and Access Shrink?

We live immersed in a world of big data. Every Internet click is tracked and noted—where we browse, what we view, how long we browse, where we shop, what we buy, what we “like.” In fact, many of our daily activities are monitored. Surveillance cameras track vehicles as they pass through intersections and track pedestrians as they walk down sidewalks; cameras note our transactions at ATMs. While it’s understandable to worry that so much surveillance invades our privacy, we may be overlooking the most personal information of all: the order of base pairs in our genomes.

O Privacy, Where Art Thou?: Protecting Privacy and Confidentiality in an Era of Big Data Access

“Big Brother Is Watching You,” George Orwell, 1984 Orwell's words ring true. Big Data mean that a new analytical paradigm is open to statisticians and social scientists (see a discussion by Hey, Ta...

Analysis and optimization for RAID small data random access

In order to make multiple disks accessible parallelly,the stripe layout is adopted by RAID for high-bandwidth.The approach is appropriate for big sequential data access,but is not that effective for small data random access.This paper analyzed the I/O performance based on RAID Stripe size,explored the IOPS(IOs per second)of multiple user small data access,and proposed a new stripe layout architecture—Thick Granularity Model.The simulation indicates:the I/O performance of Thick Granularity Model has been improved,especially in small data random access.