Default Privacy Setting Prediction by Grouping User’s Attributes and Settings Preferences

Nakamura, Tôru; Tesfay, Welderufael B.; Kiyomoto, Shinsaku; Serna, José Antonio de la O

doi:10.1007/978-3-319-67816-0_7

Cited by 4 publications

(6 citation statements)

References 19 publications

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“…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.…”

Section: Requirement Elicitationmentioning

confidence: 99%

“…The first requirement is to find a method to optimize the selection of the best combination of n out of the 80 features for questioning the user. Table 5 for Model 1 and Table 6 for Model 2 show that the selection is done randomly and not user-specific in the previous approaches [31,32,33].…”

Section: • R1 Appropriate Feature Selectionmentioning

confidence: 99%

“…In general, all papers aim to provide a solution to reduce the burden of privacy setting for web-based services according to the user's privacy preferences. Therefore, the first papers [31,32] propose a machine learning-based approach for default privacy setting prediction that is further improved in the third paper [33]. In the fourth paper [34] the model is tested and evaluated in an user experiment.…”

Section: Previous Approachesmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Explainable Machine Learning for Default Privacy Setting Prediction

et al. 2021

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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.

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“…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.…”

Section: Requirement Elicitationmentioning

confidence: 99%

Section: • R1 Appropriate Feature Selectionmentioning

confidence: 99%