scholarly journals Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

2020 ◽  
Vol 8 (4S5) ◽  
pp. 155-158
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Big Data Analytics ◽  
Processing System ◽  
User Privacy ◽  
Big Data Processing ◽  
Encrypted Data ◽  
Cipher Text ◽  
Private Data ◽  
Single User System

Big data offers various services like storing sensitive, private data and maintaining the data. Big data users may upload encrypted data rather than raw data for preserving data. Processing and analyzing the encrypted data is the primary target for attackers and hackers. Homomorphic Re-Encryption to supports access control, processed cipher-text on encrypted data and ensure data confidentiality. However, the limitation of Homomorphic Re-Encryption is the single-user system, which means it allows the party that owns a homomorphic decryption key to decrypt processed cipher-texts. Original Homomorphic Re-Encryption cannot support multiple users to access the processed cipher texts flexibly. In this paper, propose a Privacy-Preserving Big Data Processing system which support of a Homomorphic Re-Encryption using laplacian phase that extends partially from a single-group user system by offering cipher text re-encryption that allows accessing processed cipher-texts. Through the cooperation of a Data Provider, to increase the flexibility and security of our system, However apply multiple Services to take in charge of the data from their users and design computing operations over cipher-texts belonging to multiple Service. The analysis completed on proves that our Preserving the Privacy of Big Data Processing method’s to performance in terms of security is good on some datasets, inefficiency this also ensures the security and user privacy.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

2015 ◽  
pp. 289-312
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

Big Data ◽  
2016 ◽  
pp. 418-440
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

scholarly journals Influence of AI and Machine Learning in Insurance Sector

2022 ◽  
Author(s):  
Nitin Prajapati
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Quantitative Research ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Insurance Companies ◽  
Big Data Processing ◽  
Insurance Sector ◽  
Sector Analysis

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

scholarly journals Real-time Big Data Processing System to Improve Semiconductor Production Efficiency in Smart Factory

2018 ◽  
Vol 7 (3.33) ◽  
pp. 243
Author(s):  
Hyeopgeon Lee ◽  
Young-Woon Kim ◽  
Ki-Young Kim
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Production Line ◽  
Production Efficiency ◽  
Processing System ◽  
Data Processing System ◽  
Big Data Processing ◽  
Defect Rate ◽  
Semiconductor Production

Semiconductor production efficiency is closely related to the defect rate in the production process. The temperature and humidity control in the production line are very important because these affect the defect rate. So many smart factory of semiconductor production uses sensor. It is installed in the semiconductor process, which send huge amounts of data per second to a central server to carry out temperature and humidity control in each production line. However, big data processing systems that analyze and process large-scale data are subject to frequent delays in processing, and transmitted data are lost owing to bottlenecks and insufficient memory caused by traffic concentrated in the central server. In this paper, we propose a real-time big data processing system to improve semiconductor production efficiency. The proposed system consists of a production line collection system, task processing system and data storage system, and improves the productivity of the semiconductor manufacturing process by reducing data processing delays as well as data loss and discarded data.  

Big Data Overview

Big Data ◽  
2016 ◽  
pp. 1-29 ◽  
Author(s):  
Yushi Shen ◽  
Yale Li ◽  
Ling Wu ◽  
Shaofeng Liu ◽  
Qian Wen
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Processing ◽  
Data Scientist ◽  
Definition Of

This chapter provides an overview of big data and its environment and opportunities. It starts with a definition of big data and describes the unique characteristics, structure, and value of big data, and the business drivers for big data analytics. It defines the role of the data scientist and describes the new ecosystem for big data processing and analysis.

Design of Big Data Processing System Architecture Based on Hadoop under the Cloud Computing

2014 ◽  
Vol 556-562 ◽  
pp. 6302-6306 ◽  
Author(s):  
Chun Mei Duan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
System Architecture ◽  
Processing System ◽  
System Stability ◽  
Security Model ◽  
Data Processing System ◽  
Big Data Processing

In allusion to limitations of traditional data processing technology in big data processing, big data processing system architecture based on hadoop is designed, using the characteristics of quantification, unstructured and dynamic of cloud computing.It uses HDFS be responsible for big data storage, and uses MapReduce be responsible for big data calculation and uses Hbase as unstructured data storage database, at the same time a system of storage and cloud computing security model are designed, in order to implement efficient storage, management, and retrieval of data,thus it can save construction cost, and guarantee system stability, reliability and security.

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