TY  - JOUR
A1  - Löbner, Sascha
A1  - Tesfay, Welderufael Berhane
A1  - Nakamura, Toru
A1  - Pape, Sebastian
T1  - Explainable machine learning for default privacy setting prediction
T2  - IEEE Xplore digital library
N2  - When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well-suited. Such tools aim to lower the burden to set privacy preferences according to owners’ privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations – such as the General Data Protection Regulation (GDPR) in Europe – in this paper an explainable model for default privacy setting prediction is introduced. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model’s design before it goes into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
KW  - privacy preference
KW  - privacy setting
KW  - machine learning
KW  - explainability
KW  - interpretability
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/61240
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-612402
SN  - 2473-2001
VL  - 9
SP  - 63700
EP  - 63717
PB  - IEEE
CY  - New York
ER  -