 Identity DeceptionKazuhiko Shibuya
Chapter Chapter 7 in Digital Transformation of Identity in the Age of Artificial Intelligence, 2020, pp 99-110 from Springer Abstract: Abstract This chapter deals with identity deception. How do those who pretend to someone detect by appropriate ways? How to detect deceptive communications among numerous participants in security context? It is always one of the old and new problems in computer security (e.g., verification on counterfactual data, monitoring, detection, authentication, and protection). In this chapter, the author mainly takes up some cases. First issue is to design a mathematical model on anomaly detections in mutual communications among participants in the larger networking. Scoring services driven by the AI have been focused on daily activities to measure each digitized score of the citizens on the bases of their honesty and other dispositions. And blockchain distributed networking ought to serve mutual monitoring and detection mechanisms among multiple participants. Secondly, such deceptive behavior can be refined as a problem on social intelligence of the human for adaptations. Finally, deceptive technology on generative adversarial machine learning should be discussed. Date: 2020 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-15-2248-2_7 Ordering information: This item can be ordered from DOI: 10.1007/978-981-15-2248-2_7 Access Statistics for this chapter More chapters in Springer Books from Springer |
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