Probably Secure Keyed-Function Based Authenticated Encryption Schemes for Big Data

2017 ◽  
Vol 28 (06) ◽  
pp. 661-682
Author(s):  
Rashed Mazumder ◽  
Atsuko Miyaji ◽  
Chunhua Su
Keyword(s):  
Big Data ◽  
Research Area ◽  
Security Model ◽  
Authenticated Encryption ◽  
Big Data Applications ◽  
Critical Issues ◽  
Encryption Schemes ◽  
Big Data Application ◽  
Probabilistic Encryption ◽  
Security Bound

Security, privacy and data integrity are the critical issues in Big Data application of IoT-enable environment and cloud-based services. There are many upcoming challenges to establish secure computations for Big Data applications. Authenticated encryption (AE) plays one of the core roles for Big Data’s confidentiality, integrity, and real-time security. However, many proposals exist in the research area of authenticated encryption. Generally, there are two concepts of nonce respect and nonce reuse under the security notion of the AE. However, recent studies show that nonce reuse needs to sacrifice security bound of the AE. In this paper, we consider nonce respect scheme and probabilistic encryption scheme which are more efficient and suitable for big data applications. Both schemes are based on keyed function. Our first scheme (FS) operates in parallel mode whose security is based on nonce respect and supports associated data. Furthermore, it needs less call of functions/block-cipher. On the contrary, our second scheme is based on probabilistic encryption. It is expected to be a light solution because of weaker security model construction. Moreover, both schemes satisfy reasonable privacy security bound.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

2022 ◽  
pp. 1-11
Author(s):  
Bernard Tuffour Atuahene ◽  
Sittimont Kanjanabootra ◽  
Thayaparan Gajendran
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Construction Projects ◽  
Big Data Applications ◽  
Data Application ◽  
Construction Firm ◽  
Big Data Application ◽  
Tangible Benefit ◽  
Design Construction

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

scholarly journals Federated Learning in Big Data Application and Sharing

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Kunpeng Liu ◽  
Peng Jin ◽  
Guoyi Zhao
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
The State ◽  
Ideal Model ◽  
Local Data ◽  
Data Safety ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application ◽  
Uniform Model

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.

Data Literacy

2017 ◽  
Vol 8 (2) ◽  
pp. 14-38 ◽  
Author(s):  
Dimitar Christozov ◽  
Katia Rasheva-Yordanova
Keyword(s):  
Big Data ◽  
Domain Knowledge ◽  
Real Life ◽  
Big Data Analytics ◽  
Point Of View ◽  
Life Problems ◽  
Big Data Applications ◽  
Master Data ◽  
Big Data Application ◽  
Data Driven Decisions

The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.

scholarly journals Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

2020 ◽  
Vol 10 (23) ◽  
pp. 8524
Author(s):  
Cornelia A. Győrödi ◽  
Diana V. Dumşe-Burescu ◽  
Doina R. Zmaranda ◽  
Robert Ş. Győrödi ◽  
Gianina A. Gabor ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Database Systems ◽  
Application Performance ◽  
Big Data Applications ◽  
Database Technology ◽  
Important Challenge ◽  
Big Data Application ◽  
The Impact

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

scholarly journals A Survey on Job Scheduling in Big Data

2016 ◽  
Vol 16 (3) ◽  
pp. 35-51 ◽  
Author(s):  
M. Senthilkumar ◽  
P. Ilango
Keyword(s):  
Big Data ◽  
Job Scheduling ◽  
Research Area ◽  
Data Locality ◽  
Distributed Data ◽  
Distributed Data Processing ◽  
Big Data Applications ◽  
Mapreduce Model ◽  
Active Research ◽  
And Behavior

Abstract Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different existing algorithms with advantages, drawbacks, limitations. In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce. This paper helps the beginners and researchers in understanding the scheduling mechanisms used in Big Data.

scholarly journals Exploration on the Construction of Sharing Platform of Power Big Data Application

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Lihua Gong ◽  
Longzhu Zhu ◽  
Kunpeng Liu
Keyword(s):  
Big Data ◽  
Big Data Applications ◽  
Data Application ◽  
Application Sharing ◽  
Big Data Application

Big data application sharing supermarket is a big data application sharing portal. The first, one or more enterprise users can share and use the same big data application product. This product comes from the contribution of enterprise users of a certain sharing supermarket. In this way, we can solve the problem of unbalanced development of big data capabilities, and avoid repeated construction and repeated investment in the same kind of big data applications; the second. Through such a sharing platform, many enterprises carry out cooperation such as federal learning to jointly create big data application products.

Resource Provisioning and Scheduling of Big Data Processing Jobs

2018 ◽  
pp. 382-401
Author(s):  
Rajni Aron ◽  
Deepak Kumar Aggarwal
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Research Area ◽  
Resource Provisioning ◽  
Time Data ◽  
Big Data Processing ◽  
Cloud Resource Management ◽  
Big Data Applications ◽  
Cloud Resource

Cloud Computing has become a buzzword in the IT industry. Cloud Computing which provides inexpensive computing resources on the pay-as-you-go basis is promptly gaining momentum as a substitute for traditional Information Technology (IT) based organizations. Therefore, the increased utilization of Clouds makes an execution of Big Data processing jobs a vital research area. As more and more users have started to store/process their real-time data in Cloud environments, Resource Provisioning and Scheduling of Big Data processing jobs becomes a key element of consideration for efficient execution of Big Data applications. This chapter discusses the fundamental concepts supporting Cloud Computing & Big Data terms and the relationship between them. This chapter will help researchers find the important characteristics of Cloud Resource Management Systems to handle Big Data processing jobs and will also help to select the most suitable technique for processing Big Data jobs in Cloud Computing environment.

Database Systems for Big Data Storage and Retrieval

2018 ◽  
pp. 76-100 ◽  
Author(s):  
Venkat Gudivada ◽  
Amy Apon ◽  
Dhana L. Rao
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Database Systems ◽  
Query Languages ◽  
Research Issues ◽  
Storage And Retrieval ◽  
Emerging Trends ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Special needs of Big Data applications have ushered in several new classes of systems for data storage and retrieval. Each class targets the needs of a category of Big Data application. These systems differ greatly in their data models and system architecture, approaches used for high availability and scalability, query languages and client interfaces provided. This chapter begins with a description of the emergence of Big Data and data management requirements of Big Data applications. Several new classes of database management systems have emerged recently to address the needs of Big Data applications. NoSQL is an umbrella term used to refer to these systems. Next, a taxonomy for NoSQL systems is developed and several NoSQL systems are classified under this taxonomy. Characteristics of representative systems in each class are also discussed. The chapter concludes by indicating the emerging trends of NoSQL systems and research issues.

scholarly journals Smart Surveillance System using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 1151-1155
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Research Area ◽  
Violence Detection ◽  
Detection And Identification ◽  
Big Data Applications ◽  
Equal Importance ◽  
Smart Surveillance ◽  
Smart Surveillance System ◽  
Media Data

In industry and research area big data applications are consuming most of the spaces. Among some examples of big data, the video streams from CCTV cameras as equal importance with other sources like medical data, social media data. Based on the security purpose CCTV cameras are implemented in all places where security having much importance. Security can be defined in different ways like theft identification, violence detection etc. In most of the highly secured areas security plays a major role in a real time environment. This paper discusses the detecting and recognising the facial features of the persons using deep learning concepts. This paper includes deep learning concepts starts from object detection, action detection and identification. The issues recognized in existing methods are identified and summarized.

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