Easing the Burden of Setting Privacy Preferences: A Machine Learning Approach

Explainable Machine Learning for Default Privacy Setting Prediction

“…As shown in Table 3 the participants of the survey are uniformly distributed with regard to gender and age. Investigations on the digital nativity of users as introduced by [40] did not have a significant impact [31]. Table 4 informs about the used devices the online survey was accessed from.…”

“…The aim of this chapter is to line out open research questions of the preceding papers of Nakamura et al [31,32,33] that form the starting point of the current paper.…”

“…Moreover, not accepting the settings recommended by the service provider almost always ends in the denial of the service. Besides this, most users are overstrained or even not aware of their self-privacy preferences [31]. Solove [45] claims that the principle of privacy self-management that includes the rights to notice, access and consent to the collection, use, and disclosure of personal data is beyond the users' capabilities and almost always ends in insufficient 1 Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) OJ L119/1.…”

“…A tool fulfilling these requirements besides providing explainability was already proposed by Nakamura et al [31,32,33]. Their two models can automatically predict 75 unknown privacy features from 5 features received from the user with an accuracy of 82 % and 85 % and are trained and tested based on a survey with 10,000 Japanese participants.…”

“…In Section III we explain the methodology of the paper. In Section IV, we compare the previous work of Nakamura et al [31,32,33], and in Section III-C we elicit the requirements investigated in this paper. In Section V we present our approach including the requirements of Section III-C.…”