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Protecting Your Online Privacy: Insights on Digital Twins and Threat Detection

Authors:
Sergej Schultenkämper
Frederik Simon Bäumer

Keywords: Digital Twin; Privacy; Social Networks

Abstract:
This paper presents considerations for the use of Digital Twins for protecting online privacy and detecting potential threats. While Digital Twins offer a promising approach to modeling individual vulnerability and identifying online threats, there are still significant challenges to be addressed. One of the primary challenges is the need for diverse and comprehensive training data, as Digital Twin instantiation relies heavily on machine learning algorithms. To address this issue, the authors describe two datasets for Digital Twin instantiation based on Computer Vision and Natural Language Processing techniques. In addition, the authors also examine the limitations of current approaches for creating Digital Twins and propose potential areas for future research. The main objective of this work is to provide insights and considerations for the use of Digital Twins in online privacy protection and threat detection.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2023

Publication date: June 26, 2023

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-68558-049-0

Location: Nice, France

Dates: from June 26, 2023 to June 30, 2023