Security and privacy in mobile crowdsourcing networks: challenges and opportunities

2015 ◽  
Vol 53 (8) ◽  
pp. 75-81 ◽  
Author(s):  
Kan Yang ◽  
Kuan Zhang ◽  
Ju Ren ◽  
Xuemin Shen
IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 72033-72036 ◽  
Author(s):  
Debiao He ◽  
Kim-Kwang Raymond Choo ◽  
Neeraj Kumar ◽  
Aniello Castiglione

Author(s):  
Siddharth M. Nair ◽  
Varsha Ramesh ◽  
Amit Kumar Tyagi

The major issues and challenges in blockchain over internet of things are security, privacy, and usability. Confidentiality, authentication, and control are the challenges faced in security issue. Hence, this chapter will discuss the challenges and opportunities from the prospective of security and privacy of data in blockchain (with respect to security and privacy community point of view). Furthermore, the authors will provide some future trends that blockchain technology may adapt in the near future (in brief).


The Batuk ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 37-51
Author(s):  
Sanita Mastran

This descriptive study aims at exploring the challenges and opportunities of e-banking in the Nepalese banking sector. The required data are collected from bank employees by applying a self administered questionnaire, semi-structured interviews and the desktop research. The findings demonstrate that banks expand to e-banking services in order to remain competitive, to update themselves with new technological developments and to minimize transaction cost and to facilitate customers. The major challenges faced the e-banking customers are non-familiarity with advanced technology, internet connection problems, problems regarding security and privacy. These challenges have a negative influence on the adoption of e-banking services by customers in Nepal. To overcome the challenges, Nepalese banking industry should invest on adopting the most secured and trustworthy e-banking system and educating customers on the use and importance of e-banking regularly.


Author(s):  
Atiqur Rahman ◽  
Guangfu Wu ◽  
Ali Md Liton

Nowadays, the masonry for environment-friendly and protected network structure designs, for example, the Internet of Things and gigantic data analytics are increasing at a faster pace compared to an earlier state. Mobile edge computing for an Internet of Things widget is information processing that is achieved at or close to the collectors of information in an Internet of Things system. Herein, we are proposing to temporarily evaluation the concepts, features, protection, and privacy applications of Internet of Things authorized mobile edge computing with its data protection view in our data-driven globe. We focus on illuminating one of kind components that need to be taken into consideration whilst creating a scalable, consistent, impenetrable and disseminated mobile edge computing structure. We also sum up the fundamental ideas regarding security threat alleviation strategies. After that, we walk around the existing challenges and opportunities in the area of mobile edge computing. In conclusion, we analyze a case study, in which a security protection mechanism can be hardened to lift out everyday jobs.


2021 ◽  
Vol 20 (4) ◽  
pp. 82-86
Author(s):  
Florian Alt ◽  
Florian Alt ◽  
Florian Schaub

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 46134-46145 ◽  
Author(s):  
Lei Cui ◽  
Gang Xie ◽  
Youyang Qu ◽  
Longxiang Gao ◽  
Yunyun Yang

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6230 ◽  
Author(s):  
Ji Chu Jiang ◽  
Burak Kantarci ◽  
Sema Oktug ◽  
Tolga Soyata

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 34564-34584 ◽  
Author(s):  
Maanak Gupta ◽  
Mahmoud Abdelsalam ◽  
Sajad Khorsandroo ◽  
Sudip Mittal

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