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Privacy Risk Assessment of Individual Psychometric Profiles

Authors :
Giacomo Mariani
Francesca Naretto
Anna Monreale
Source :
Discovery Science. DS 2021. Lecture Notes in Computer Science, Discovery Science ISBN: 9783030889418, DS, Lecture Notes in Computer Science, Lecture Notes in Computer Science-Discovery Science
Publication Year :
2021
Publisher :
Springer Science and Business Media Deutschland GmbH, 2021.

Abstract

In the modern Internet era the usage of social media such as Twitter and Facebook is constantly increasing. These social media are accumulating a lot of textual data, because individuals often use them for sharing their experiences and personal facts writing text messages. These data hide individual psychological aspects that might represent a valuable alternative source with respect to the classical clinical texts. In many studies, text messages are used to extract individuals psychometric profiles that help in analysing the psychological behaviour of users. Unfortunately, both text messages and psychometric profiles may reveal personal and sensitive information about users, leading to privacy violations. Therefore, in this paper, we propose a study of privacy risk for psychometric profiles: we empirically analyse the privacy risk of different aspects of the psychometric profiles, identifying which psychological facts expose users to an identity disclosure.

Details

Language :
English
ISBN :
978-3-030-88941-8
978-3-030-88942-5
ISSN :
03029743 and 16113349
ISBNs :
9783030889418 and 9783030889425
Database :
OpenAIRE
Journal :
Discovery Science. DS 2021. Lecture Notes in Computer Science, Discovery Science ISBN: 9783030889418, DS, Lecture Notes in Computer Science, Lecture Notes in Computer Science-Discovery Science
Accession number :
edsair.doi.dedup.....c1f8f82f9220257357a91b53b579ae09