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Privacy and its protection is an important part of the culture in the USA and Europe. Literature in this field lacks empirical data from Japan. Thus, it is difficult– especially for foreign researchers – to understand the situation in Japan. To get a deeper understanding we examined the perception of a topic that is closely related to privacy: the perceived benefits of sharing data and the willingness to share in respect to the benefits for oneself, others and companies. We found a significant impact of the gender to each of the six analysed constructs.
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.