@conference{Drosatos2020219,
title = {Privacy-preserving solutions in the Industrial Internet of Things},
author = { G. Drosatos and K. Rantos and D. Karampatzakis and T. Lagkas and P. Sarigiannidis},
url = {https://www.researchgate.net/publication/344057084_Privacy-preserving_solutions_in_the_Industrial_Internet_of_Things},
doi = {10.1109/DCOSS49796.2020.00044},
year = {2020},
date = {2020-01-01},
journal = {Proceedings - 16th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2020},
pages = {219-226},
abstract = {Industrial Internet of Things (IIoT) is a relatively new area of research that utilises multidisciplinary and holistic approaches to develop smart solutions for complex problems in industrial environments. Designing applications for the IIoT is a non trivial issue and requires to address, among many others, technology concerns, the protection of personal data, and the privacy of individuals. In this review paper, we identify privacy-preserving solutions that have been proposed in the literature to safeguard the privacy of individuals being part, or interacting with, the IIoT environment. As such, it considers two main categories of the analysed protocols, i.e., the privacy-preserving data management and processing solutions, and the privacy-preserving authentication methods. © 2020 IEEE.},
keywords = {Industrial Internet of Things (IIoT), Literature review, Privacy, Privacy-preserving authentication methods, Privacy-preserving solutions},
pubstate = {published},
tppubtype = {conference}
}
