C4PS - Helping Facebookers Manage Their Privacy Settings

Paul, Thomas; Stopczynski, Martin; Puscher, Daniel; Volkamer, Melanie; Strufe, Thorsten

doi:10.1007/978-3-642-35386-4_15

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…While a larger sample is desirable, methodology and resource constraints made that infeasible. Such limitations are unfortunately common; many Facebook human subjects experiments, including some thematically close to ours, are restricted to samples of 20-60 users (e.g., [25,12,27,39,14,8]). Because our use of machine learning utilized data on a per-user basis (i.e., training and testing took place on a single individual's data), a larger sample size would not have directly benefited the efficacy of learning, but it would increase the likelihood that the sample was representative of the Facebook population.…”

Section: Limitationsmentioning

confidence: 98%

What you want is not what you get

Sinha

Bauer

2013

Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security

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As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand the privacy features offered by social networks.A way to mitigate these problems is to develop automated tools to assist users in correctly setting their policy. This paper explores the viability of one such approach: we examine the extent to which machine learning can be used to deduce users' sharing preferences for content posted on Facebook. To generate data on which to evaluate our approach, we conduct an online survey of Facebook users, gathering their Facebook posts and associated policies, as well as their intended privacy policy for a subset of the posts. We use this data to test the efficacy of several algorithms at predicting policies, and the effects on prediction accuracy of varying the features on which they base their predictions. We find that Facebook's default behavior of assigning to a new post the privacy settings of the preceding one correctly assigns policies for only 67% of posts. The best of the prediction algorithms we tested outperforms this baseline for 80% of participants, with an average accuracy of 81%; this equates to a 45% reduction in the number of posts with misconfigured policies. Further, for those participants (66%) whose implemented policy usually matched their intended policy, our approach predicts the correct privacy settings for 94% of posts.

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Section: Limitationsmentioning

confidence: 98%

What you want is not what you get

Sinha

Bauer

2013

Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security

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“…Whereas, the "Content feedback" nudge was perceived as needles and not very positive nudge, as participants were feeling being judged by the tool. Paul et al [86] studied an enhanced interface for the Facebook privacy settings. They proposed a coloured representation of the privacy settings (C4PS -Colours for Privacy Settings), to demonstrate the visibility of the profile items of users, such as photos.…”

Section: Related Workmentioning

confidence: 99%

Collateral damage of Facebook third-party applications: a comprehensive study

Symeonidis

Biczók

Shirazi

et al. 2018

Computers & Security

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Third-party applications on Facebook can collect personal data of the users who install them, but also of their friends. This raises serious privacy issues as these friends are not notified by the applications nor by Facebook and they have not given consent. This paper presents a detailed multi-faceted study on the collateral information collection of the applications on Facebook. To investigate the views of the users, we designed a questionnaire and collected the responses of 114 participants. The results show that participants are concerned about the collateral information collection and in particular about the lack of notification and of mechanisms to control the data collection. Based on real data, we compute the likelihood of collateral information collection affecting users: we show that the probability is significant and greater than 80% for popular applications such as TripAdvisor. We also demonstrate that a substantial amount of profile data can be collected by applications, which enables application providers to profile users. To investigate whether collateral information collection is an issue to users' privacy we analysed the legal framework in light of the new General Data Protection Regulation. We provide a detailed analysis of the entities involved and investigate which entity is accountable for the collateral information collection. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency towards collateral information collection. Furthermore, we discuss alternative solutions highlighting other countermeasures such as notification and access control mechanisms, cryptographic solutions and application auditing. To the best of our knowledge this is the first work that provides a detailed multi-faceted study of this problem and that analyses the threat of user profiling by application providers.

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“…comprehension like [21], [22] stressed the importance of familiarity: people are more likely to comprehend the data if it is presented using familiar concepts from the same context. The concept of applying common practices in developing usable interfaces has also been elaborated in [23].…”

Section: A Literature Reviewmentioning

confidence: 99%

Advancing Trust Visualisations for Wider Applicability and User Acceptance

Kulyk

Reinheimer

Gerber

et al. 2017

2017 IEEE Trustcom/BigDataSE/Icess

Self Cite

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There are only a few visualisations targeting the communication of trust statements. Even though there are some advanced and scientifically founded visualisations-like, for example, the opinion triangle, the human trust interface, and T-Viz-the stars interface known from e-commerce platforms is by far the most common one. In this paper, we propose two trust visualisations based on T-Viz, which was recently proposed and successfully evaluated in large user studies. Despite being the most promising proposal, its design is not primarily based on findings from human-computer interaction or cognitive psychology. Our visualisations aim to integrate such findings and to potentially improve decision making in terms of correctness and efficiency. A large user study reveals that our proposed visualisations outperform T-Viz in these factors.

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C4PS - Helping Facebookers Manage Their Privacy Settings

Cited by 13 publications

References 18 publications

What you want is not what you get

What you want is not what you get

Collateral damage of Facebook third-party applications: a comprehensive study

Advancing Trust Visualisations for Wider Applicability and User Acceptance

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