A Quick Survey of Security and Privacy Issues in Cloud and a Proposed Data-Centric Security Model for Data Security

2020 ◽  
pp. 431-443
Author(s):  
Abraham Ekow Dadzie ◽  
Shri Kant
Keyword(s):  
Data Security ◽  
Security And Privacy ◽  
Security Model ◽  
Privacy Issues

Data Security and Privacy Assurance Considerations in Cloud Computing for Health Insurance Providers

2017 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
Amavey Tamunobarafiri ◽  
Shaun Aghili ◽  
Sergey Butakov
Keyword(s):  
Cloud Computing ◽  
Health Insurance ◽  
Data Security ◽  
Assessment Tool ◽  
Security And Privacy ◽  
Data Breaches ◽  
Healthcare Data ◽  
The Us ◽  
Health Insurance Providers ◽  
Privacy Issues

Cloud computing has been massively adopted in healthcare, where it attracts economic, operational, and functional advantages beneficial to insurance providers. However, according to Identity Theft Resource Centre, over twenty-five percent of data breaches in the US targeted healthcare. The HIPAA Journal reported an increase in healthcare data breaches in the US in 2016, exposing over 16 million health records. The growing incidents of cyberattacks in healthcare are compelling insurance providers to implement mitigating controls. Addressing data security and privacy issues before cloud adoption protects from monetary and reputation losses. This article provides an assessment tool for health insurance providers when adopting cloud vendor solutions. The final deliverable is a proposed framework derived from prominent cloud computing and governance sources, such as the Cloud Security Alliance, Cloud Control Matrix (CSA, CCM) v 3.0.1 and COBIT 5 Cloud Assurance.

Challenges for Big Data Security and Privacy

2019 ◽  
pp. 57-66
Author(s):  
M. Govindarajan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Large Scale ◽  
High Volume ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Cloud Migration ◽  
Definitive Solution ◽  
Cloud Infrastructures ◽  
Privacy Issues

Security and privacy issues are magnified by the volume, variety, and velocity of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, big data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, big data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the big data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging big data security and privacy issues.

An Investigation for Cellular Automata-Based Lightweight Data Security Model Towards Possible Uses in Fog Networks

2021 ◽  
pp. 209-226
Author(s):  
Arnab Mitra ◽  
Sayantan Saha
Keyword(s):  
Internet Of Things ◽  
Cellular Automata ◽  
Data Security ◽  
Low Cost ◽  
Security And Privacy ◽  
Security Model ◽  
Physical Implementation ◽  
Security Models ◽  
Industry Applications ◽  
Computing Capacity

A lightweight data security model is of much importance in view of security and privacy of data in several networks (e.g., fog networks) where available computing units at edge nodes are often constrained with low computing capacity and limited storage/availability of energy. To facilitate lightweight data security at such constrained scenarios, cellular automata (CA)-based lightweight data security model is presented in this chapter to enable low-cost physical implementation. For this reason, a detailed investigation is presented in this chapter to explore the potential capabilities of CA-based scheme towards the design of lightweight data security model. Further, a comparison among several existing lightweight data security models ensure the effectiveness for proposed CA-based lightweight data security model. Thus, application suitability in view of fog networks is explored for the proposed CA-based model which has further potential for easy training of a reservoir of computers towards uses in IoT (internet of things)-based multiple industry applications.

Epilogue

Tap ◽  
2017 ◽  
Author(s):  
Anindya Ghose
Keyword(s):  
Mobile Technology ◽  
Data Security ◽  
Security And Privacy ◽  
Mobile Advertising ◽  
The Future ◽  
The Impact ◽  
Privacy Issues ◽  
The Way

This epilogue presents a few closing thoughts about the impact of mobile technology on society at large. It argues that the future of mobile advertising depends on a bargain that consumers and firms need to strike with each other. Both sides will have to make some investments and offer some trust for this give-and-take relationship to prosper. Consumers will need to find better ways to strike a personal balance between their lives and mobile technology. They will need to make the choice about how much they let technology intrude and inform their lives. They will hold the key to how open or private they want to be with their data. But this does not relieve businesses of their responsibilities. They need to pay attention and take their roles very seriously in this ecosystem. They should surprise and impress consumers while helping them with their needs the way a butler or a concierge would. More importantly, they need to take data security and privacy issues seriously.

Big Data Security

2015 ◽  
pp. 301-313 ◽  
Author(s):  
Fatima-Zahra Benjelloun ◽  
Ayoub Ait Lahcen
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security And Privacy ◽  
Economic Sectors ◽  
Data Intensive ◽  
New Information ◽  
Maximum Benefit ◽  
Security Challenges ◽  
Data Context ◽  
Privacy Issues

The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.

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