Explainable Machine Learning for Default Privacy Setting Prediction

Löbner, Sascha; Tesfay, Welderufael B.; Nakamura, Tôru; Pape, Sebastian

doi:10.1109/access.2021.3074676

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…Future work could also investigate PETs that are integrated into regular services, e. g., the use of machine learning to help users with the privacy preferences [42], integration of PETs into physical services such as payment and shipment for e-commerce [56], or the integration of PETs into the Internet infrastructure eliminating the users' effort to set up PETs themselves [22]. However, this would raise additional challenges as it needs to be clearly investigated if users refer to the PET part of the service or the traditional part.…”

Section: Discussionmentioning

confidence: 99%

Acceptance Factors of Privacy-Enhancing Technologies on the Basis of Tor and JonDonym

Pape

Harborth

2023

Human Factors in Privacy Research

Self Cite

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This chapter provides information about acceptance factors of privacy-enhancing technologies (PETs) based on our research why users are using Tor and JonDonym, respectively. For that purpose, we surveyed 124 Tor users (Harborth and Pape 2020) and 142 JonDonym users (Harborth Pape 2020) and did a quantitative evaluation (PLS-SEM) on different user acceptance factors. We investigated trust in the PET and perceived anonymity (Harborth et al. 2021; Harborth et al. 2020; Harborth and Pape 2018), privacy concerns, and risk and trust beliefs (Harborth and Pape 2019) based on Internet Users Information Privacy Concerns (IUIPC) and privacy literacy (Harborth and Pape 2020). The result was that trust in the PET seems to be the major driver. Furthermore, we investigated the users’ willingness to pay or donate for/to the service (Harborth et al. 2019). In this case, risk propensity and the frequency of perceived improper invasions of users’ privacy were relevant factors besides trust in the PET. While these results were new in terms of the application of acceptance factors to PETs, none of the identified factors was surprising. To identify new factors and learn about differences in users’ perceptions between the two PETs, we also did a qualitative analysis of the questions if users have any concerns about using the PET, when they would be willing to pay or donate, which features they would like to have and why they would (not) recommend the PET (Harborth et al. 2021; Harborth et al. 2020). To also investigate the perspective of companies, we additionally interviewed 12 experts and managers dealing with privacy and PETs in their daily business and identified incentives and hindrances to implement PETs from a business perspective (Harborth et al. 2018).

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Section: Discussionmentioning

confidence: 99%

Acceptance Factors of Privacy-Enhancing Technologies on the Basis of Tor and JonDonym

Pape

Harborth

2023

Human Factors in Privacy Research

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“…Focusing primarily on privacy and data protection goals, softcoding methods aim at the provision of information, thus enabling users to have control over their privacy. The most common examples of softcoding include visual presentation interfaces that enhance user choice (Schufrin et al, 2020;Vasylkovskyi et al, 2021), consent-based frameworks (Agbo & Mahmoud, 2020; Khalid et al, 2023), and privacy self-management (Lobner et al, 2021).…”

Section: Methods Of Regulation By Designmentioning

confidence: 99%

Regulation by design: features, practices, limitations, and governance implications

Prifti,

Morley,

Novelli

et al. 2024

SSRN Journal

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“…There are other types of tools to protect users' privacy on social media. Löbner et al [35] propose a tool to support users choose their privacy settings. The tool suggests more suitable default settings depending on a low number of questions, which reduces users' effort in making that decision.…”

Section: Privacy Enhancing Tools For Social Mediamentioning

confidence: 99%

“All apps do this”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content

Bracamonte

Pape

Loebner

2022

PoPETs

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Users report that they have regretted accidentally sharing personal information on social media. There have been proposals to help protect the privacy of these users, by providing tools which analyze text or images and detect personal information or privacy disclosure with the objective to alert the user of a privacy risk and transform the content. However, these proposals rely on having access to users’ data and users have reported that they have privacy concerns about the tools themselves. In this study, we investigate whether these privacy concerns are unique to privacy tools or whether they are comparable to privacy concerns about non-privacy tools that also process personal information. We conduct a user experiment to compare the level of privacy concern towards privacy tools and nonprivacy tools for text and image content, qualitatively analyze the reason for those privacy concerns, and evaluate which assurances are perceived to reduce that concern. The results show privacy tools are at a disadvantage: participants have a higher level of privacy concern about being surveilled by the privacy tools, and the same level concern about intrusion and secondary use of their personal information compared to non-privacy tools. In addition, the reasons for these concerns and assurances that are perceived to reduce privacy concern are also similar. We discuss what these results mean for the development of privacy tools that process user content.

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Explainable Machine Learning for Default Privacy Setting Prediction

Cited by 12 publications

References 34 publications

Acceptance Factors of Privacy-Enhancing Technologies on the Basis of Tor and JonDonym

Acceptance Factors of Privacy-Enhancing Technologies on the Basis of Tor and JonDonym

Regulation by design: features, practices, limitations, and governance implications

“All apps do this”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content

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