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.
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Notes
- 1.
The implementation of these attacks, written in Python 3.7, is available on Github https://github.com/karjudev/text-privacy. For conducting the experiments we used a server with 16x Intel(R) Xeon(R) Gold 5120 CPU @ 2.20 GHz (64 bits), 63 gb RAM.
- 2.
Composed of 517, 401 messages from 158 different authors.
- 3.
482,117 messages from 20,192 authors.
- 4.
230,571 messages from 20,192 authors.
- 5.
176,243 messages from 6,410 authors.
- 6.
176,207 messages from 6,410 authors.
- 7.
The number is obtained using the Sturges formula [22].
- 8.
Colloquial terms are: “mom” or “dad” and “mate” or “buddy”.
- 9.
Words like “think”, “know”, “always”, “never” and “should”.
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Acknowledgment
This work is partially supported by the European Community H2020 programme under the funding schemes: H2020-INFRAIA-2019-1: Research Infrastructure G.A. 871042 SoBigData++ (sobigdata.eu), G.A. 952215 TAILOR, G.A. 952026 Humane AI NET (humane-ai.eu).
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Mariani, G., Monreale, A., Naretto, F. (2021). Privacy Risk Assessment of Individual Psychometric Profiles. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_32
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