scholarly journals A Survey on Security Models for Data Privacy in Big Data Analytics

2018 ◽  
Vol 7 (S1) ◽  
pp. 87-89
Author(s):  
Avula Satya Sai Kumar ◽  
S. Mohan ◽  
R. Arunkumar
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Private Key ◽  
Access Structures ◽  
Security Models ◽  
Attribute Based Encryption ◽  
And Performance

As emerging data world like Google and Wikipedia, volume of the data growing gradually for centralization and provide high availability. The storing and retrieval in large volume of data is specialized with the big data techniques. In addition to the data management, big data techniques should need more concentration on the security aspects and data privacy when the data deals with authorized and confidential. It is to provide secure encryption and access control in centralized data through Attribute Based Encryption (ABE) Algorithm. A set of most descriptive attributes is used as categorize to produce secret private key and performs access control. Several works proposed in existing based on the different access structures of ABE algorithms. Thus the algorithms and the proposed applications are literally surveyed and detailed explained and also discuss the functionalities and performance aspects comparison for desired ABE systems.

scholarly journals Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing

2021 ◽  
Vol 9 (1) ◽  
pp. 16-44
Author(s):  
Weiqing Zhuang ◽  
Morgan C. Wang ◽  
Ichiro Nakamoto ◽  
Ming Jiang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Theoretical Research ◽  
Analysis Method ◽  
Theoretical Comparison ◽  
Statistical Analysis Method ◽  
And Performance ◽  
The U.S

Abstract Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

Efficient Attribute-Based Data Sharing Scheme with Hidden Access Structures

2019 ◽  
Vol 62 (12) ◽  
pp. 1748-1760 ◽  
Author(s):  
Yang Chen ◽  
Wenmin Li ◽  
Fei Gao ◽  
Wei Yin ◽  
Kaitai Liang ◽  
...  
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Inner Product ◽  
Access Structure ◽  
Online Data ◽  
Fine Grained ◽  
Access Structures ◽  
Attribute Based Encryption ◽  
And Performance ◽  
Ciphertext Policy

AbstractOnline data sharing has become a research hotspot while cloud computing is getting more and more popular. As a promising encryption technique to guarantee the security shared data and to realize flexible fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has drawn wide attentions. However, there is a drawback preventing CP-ABE from being applied to cloud applications. In CP-ABE, the access structure is included in the ciphertext, and it may disclose user’s privacy. In this paper, we find a more efficient method to connect ABE with inner product encryption and adopt several techniques to ensure the expressiveness of access structure, the efficiency and security of our scheme. We are the first to present a secure, efficient fine-grained access control scheme with hidden access structure, the access structure can be expressed as AND-gates on multi-valued attributes with wildcard. We conceal the entire attribute instead of only its values in the access structure. Besides, our scheme has obvious advantages in efficiency compared with related schemes. Our scheme can make data sharing secure and efficient, which can be verified from the analysis of security and performance.

scholarly journals I-Os in the Vanguard of Big Data Analytics and Privacy

2015 ◽  
Vol 8 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Adam J. Ducey ◽  
Nigel Guenole ◽  
Sara P. Weiner ◽  
Hailey A. Herleman ◽  
Robert E. Gibby ◽  
...  
Keyword(s):  
Big Data ◽  
Informed Consent ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Global Scale ◽  
Data Sources ◽  
Critical Elements ◽  
The World

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

A bibliometric analysis of research on Big Data analytics for business and management

2019 ◽  
Vol 57 (8) ◽  
pp. 1993-2009 ◽  
Author(s):  
Lorenzo Ardito ◽  
Veronica Scuotto ◽  
Manlio Del Giudice ◽  
Antonio Messeni Petruzzelli
Keyword(s):  
Big Data ◽  
Citation Analysis ◽  
Bibliometric Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theoretical Development ◽  
Firm Resources ◽  
Content Type ◽  
Subjective Assessments ◽  
And Performance

Purpose The purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena. Design/methodology/approach An objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided. Findings From the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management. Originality/value To the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

A Novel File Hierarchy Access Control Scheme Using Attribute-Based Encryption

2014 ◽  
Vol 701-702 ◽  
pp. 911-918 ◽  
Author(s):  
Shu Lan Wang ◽  
Jian Ping Yu ◽  
Peng Zhang ◽  
Ping Wang
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Access Structure ◽  
Secret Key ◽  
Chosen Plaintext Attack ◽  
Fine Grained ◽  
Access Structures ◽  
Diffie Hellman ◽  
Attribute Based Encryption ◽  
Control Scheme

Attribute-based encryption (ABE) can keep data privacy and realize fine-grained access control. However, the notion of file hierarchy hasn't been presented until now. The problem, the multiple hierarchical files to be shared only using once encryption scheme, cannot be effectively solved. Based on the access structure layered model, a novel access control scheme about file hierarchy is proposed by using ABE to solve the problem. The proposed scheme will not only decrease the number of access structures to one, but also only require a secret key to decrypt all the authorization files. It is proved to be secure against the chosen-plaintext attack (CPA) under the decision bilinear Diffie-Hellman (DBDH) assumption. In addition, the performance analysis results indicate that the proposed scheme is efficient and practical when a large number of hierarchical files are shared.

scholarly journals Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Processing Unit ◽  
Privacy And Security ◽  
User Data ◽  
Data Ingestion

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

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