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Privacy and Personalization. The Trade-off between Data Disclosure and Personalization Benefit

 
: Wadle, Lisa-Marie; Martin, Noemi; Ziegler, Daniel

:
Preprint urn:nbn:de:0011-n-5522649 (985 KByte PDF)
MD5 Fingerprint: bf6b7b95763fa42fed19fa99b9483b19
© ACM This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Erstellt am: 23.7.2019


Papadopoulos, George Angelos (General Chair) ; Association for Computing Machinery -ACM-:
UMAP 2019 Adjunct, 27th Conference on User Modeling, Adaptation and Personalization. Adjunct Publication : Larnaca, Cyprus, June 09 - 12, 2019
New York: ACM, 2019
ISBN: 978-1-4503-6711-0
S.319-324
Conference on User Modeling, Adaptation and Personalization (UMAP) <27, 2019, Larnaca>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAO ()
Fraunhofer IBP ()

Abstract
Personalization in principle cannot happen without information about individuals, requiring personalization systems to comply with official privacy regulations. However, in order to design personalization systems that provide the best possible privacy-related user experience, a more human-centered perspective has to be taken into account. As a first step towards this goal, in the present work we show the setup and results of an online survey investigating the relation between the intention to disclose certain categories of personal data and the type of benefit promised by personalization.

: http://publica.fraunhofer.de/dokumente/N-552264.html