Effect of data privacy and security investment on the value of big data firms

2021 ◽  
pp. 113543
Author(s):  
Yueyue Zhang ◽  
Cheng Zhang ◽  
Yunjie Xu
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy And Security ◽  
Security Investment

scholarly journals Solutions to Big Data Privacy and Security Challenges Associated With COVID-19 Surveillance Systems

2021 ◽  
Vol 4 ◽  
Author(s):  
Vibhushinie Bentotahewa ◽  
Chaminda Hewage ◽  
Jason Williams
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Personal Data ◽  
Surveillance Systems ◽  
Privacy Rights ◽  
Personal Privacy ◽  
Privacy And Security ◽  
Digital Surveillance ◽  
Feasible Solutions ◽  
The Impact

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.

scholarly journals Secure Data-Encryption Strategy for Big-Data in Mobile Cloud

2019 ◽  
Vol 9 (1S) ◽  
pp. 1-4
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Data Encryption ◽  
Mobile Cloud ◽  
Timing Constraints ◽  
Privacy And Security ◽  
Secure Data ◽  
Consistent Performance ◽  
A New Technique ◽  
High Level

Now-a-days data plays a key role in Information Technology and while coming to privacy of that data it has become a considerable issue to maintain data security at high level. Large amounts of data generated through devices are considered as a major obstacle and also tough to handle in real time scenarios. To meetwith consistent performance applications at present abandon encryptions techniquesbecausethe time for the execution and the completion of encryption techniques plays a key role during processing and transmissions of data. In this paper our moto is to secure data and proposed a new technique called Dynamic Data Encryption Strategy (DDES)which selectively encrypts data and uses some algorithms which provides a perfect encryption strategy for the data packages under some timing constraints. By this method we can achieve data privacy and security for big-data in mobile cloud-computing by using an encryption strategy respective to their requirements during execution time.

A Synergetic Model for Implementing Big Data in Organizations

2018 ◽  
pp. 281-298
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

Security and Privacy in Big Data Computing

2020 ◽  
pp. 236-256
Author(s):  
Kiritkumar J. Modi ◽  
Prachi Devangbhai Shah ◽  
Zalak Prajapati
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Cloud Data ◽  
Big Data Privacy ◽  
Cloud Data Storage ◽  
Type Data ◽  
Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

A Study of the Cryptographic Technologies on Big Data Privacy and Security in E-Commerce

2019 ◽  
Vol 16 (8) ◽  
pp. 3587-3590
Author(s):  
Raheem Mafas ◽  
Manoj Jayabalan
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
State Of The Art ◽  
Not Given ◽  
Privacy And Security ◽  
Comprehensive Review ◽  
Goods And Services ◽  
Big Data Privacy ◽  
Reasonable Cost ◽  
Challenges And Opportunities

In this era, big data is the most common buzzword across different industries due to its capabilities of collecting, processing, storing and analysing data. The advancement of the E-Commerce paved the way for merchants and customers to meet online to satisfy their requirements by exchanging goods and services at a reasonable cost. The challenges and opportunities for big data on the emphasis of data privacy and security is a widely discussed topic among businesses especially E-Commerce merchants. There are several reviews available on emphasizing big data opportunities and challenges with regard to privacy and security. However, a comprehensive review on E-Commerce highlighting thematically on the tools and technologies is not given enough consideration. Therefore, the purpose of this study is to review the state-of-the-art technologies towards privacy and security in the E-Commerce platforms. The identified cryptographic technologies were also discussed with the rational standpoint to understand the viability to apply in the E-Commerce operations. The study concludes with an enlightening path from which the E-Commerce merchants can be vigilant on data privacy and security in future.

scholarly journals Big Data and Analytics in Retailing

2019 ◽  
Vol 11 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Venky Shankar
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Dynamic Pricing ◽  
Data Privacy ◽  
Data Science ◽  
Shopping Behavior ◽  
Computer Algorithms ◽  
Multiple Sources ◽  
Privacy And Security ◽  
Customer Data

AbstractBig data are taking center stage for decision-making in many retail organizations. Customer data on attitudes and behavior across channels, touchpoints, devices and platforms are often readily available and constantly recorded. These data are integrated from multiple sources and stored or warehoused, often in a cloud-based environment. Statistical, econometric and data science models are developed for enabling appropriate decisions. Computer algorithms and programs are created for these models. Machine learning based models, are particularly useful for learning from the data and making predictive decisions. These machine learning models form the backbone for the generation and development of AI-assisted decisions. In many cases, such decisions are automated using systems such as chatbots and robots.Of special interest are issues such as omnichannel shopping behavior, resource allocation across channels, the effects of the mobile channel and mobile apps on shopper behavior, dynamic pricing, data privacy and security. Research on these issues reveals several interesting insights on which retailers can build. To fully leverage big data in today’s retailing environment, CRM strategies must be location specific, time specific and channel specific in addition to being customer specific.

A Synergetic Model for Implementing Big Data in Organizations

2017 ◽  
Vol 30 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

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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