A Theory of Vagueness and Privacy Risk Perception

Bhatia, Jaspreet; Breaux, Travis D.; Reidenberg, Joël R.; Norton, Thomas B.

doi:10.1109/re.2016.20

Cited by 54 publications

(59 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To obtain a more balanced corpus, a filtering step is used to select only sentences that have a moderate-to-high chance of containing vague content. Fortunately, Bhatia et al (2016) provide a list of 40 cue words for vagueness, manually compiled by policy experts. We therefore retain only sentences containing one of the cue words for further annotation.…”

Section: The Corpusmentioning

confidence: 99%

“…The coefficient λ C is set to {−1, 0, 1, 2} respectively for 'clear,' 'somewhat clear,' 'vague,' and 'extremely vague' categories. v is initialized as a binary vector, where an entry is set to 1 if it is one of the 40 cue words for vagueness (Bhatia et al, 2016). Word embeddings are initialized to their word2vec embeddings and are made trainable during the entire training process.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…They are lengthy and difficult to read. Bhatia et al (2016) suggested two possible causes for this. First, privacy policies must be comprehensive in order to cover a variety of uses (e.g., instore and online purchases).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Vague Words and Sentences in Privacy Policies

Lebanoff¹,

Liu²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.

show abstract

Section: The Corpusmentioning

confidence: 99%

Section: Parameter Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of Vague Words and Sentences in Privacy Policies

Lebanoff¹,

Liu²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…CI annotation analysis also allow us to identify information flows that use vague terminology as defined in [6] ( Table 4). Figure 5 shows the percentage of flows in Facebook's previous and updated policies that use vague terminology.…”

Section: Vague and Ambiguous Flowsmentioning

confidence: 99%

“…Numeric Quantifier Vague numeric quantifier "certain", "most", "majority", "many", "some" "few" Table 4. Summary of four vagueness categories as defined in [6] and associated example terms. Table 4)…”

Section: Modalitymentioning

confidence: 99%

Analyzing Privacy Policies Using Contextual Integrity Annotations

et al. 2018

View full text Add to dashboard Cite

In this paper, we demonstrate the effectiveness of using the theory of contextual integrity (CI) to annotate and evaluate privacy policy statements. We perform a case study using CI annotations to compare Facebook's privacy policy before and after the Cambridge Analytica scandal. The updated Facebook privacy policy provides additional details about what information is being transferred, from whom, by whom, to whom, and under what conditions. However, some privacy statements prescribe an incomprehensibly large number of information flows by including many CI parameters in single statements. Other statements result in incomplete information flows due to the use of vague terms or omitting contextual parameters altogether. We then demonstrate that crowdsourcing can effectively produce CI annotations of privacy policies at scale. We test the CI annotation task on 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. The resulting high precision annotations indicate that crowdsourcing could be used to produce a large corpus of annotated privacy policies for future research.

show abstract

Dispositional mindfulness as an antecedent of privacy concerns: A protection motivation theory perspective

Ioannou

Tussyadiah

Marshan

2021

Psychology and Marketing

View full text Add to dashboard Cite

This study investigates the effects of mindfulness, an important personality trait, on people's perceptions of privacy. Using protection motivation theory as a conceptual foundation, the central tenet is that mindfulness plays an important role in people's threat appraisal process of privacy concerns and thus influences one's intention to share personal information online. A survey‐based approach was employed to measure privacy attitudes of 685 UK individuals about online data disclosure. Our findings demonstrate that mindfulness contributes to the formation of privacy concerns. A more mindful consumer is more likely to adopt a more objective appraisal style, interpret privacy threats as less threatening, and thus share personal information online.

show abstract

A Theory of Vagueness and Privacy Risk Perception

Cited by 54 publications

References 24 publications

Automatic Detection of Vague Words and Sentences in Privacy Policies

Automatic Detection of Vague Words and Sentences in Privacy Policies

Analyzing Privacy Policies Using Contextual Integrity Annotations

Dispositional mindfulness as an antecedent of privacy concerns: A protection motivation theory perspective

Contact Info

Product

Resources

About