Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing

By Jialiu Lin, Norman M. Sadeh, Shahriyar Amini, Janne Lindqvist, Jason I. Hong, Joy Ying Zhang. Ubiquitous Computing, 2012

559 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6b04fba371a8e50a9c9b158a1645f4d6425d5cff

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Citations in the corpus

Your Location has been Shared 5,398 Times!: A Field Study on Mobile App Privacy Nudging (2015, unknown): “Mobile; Privacy; Privacy Decision Making; Privacy Nudges.”
: Privacy on Mobile Devices – It ’ s Complicated (2016, background): “Previous research has shown that users often did not understand the installtime permissions dialogs and were often surprised by the data that these over-permissioned apps could access [29, 91, 92].”
Leakiness and creepiness in app space: perceptions of privacy and mobile app use (2014, background): “Similar to findings in [25] they also indicated that these privacy concerns were often overridden by other factors when they made decisions about whether to install a particular application.”
Privacy as part of the app decision-making process (2013, background): “The majority of work done on user expectations related to this Android access control system has been done by our own group at Carnegie Mellon [12, 17] and two separate teams at Berkeley.”
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020, background): “uter Interaction (HCI) and Science and Technology studies (STS) scholars have long highlighted the struggle of technologists to identify and incorporate these factors into their development processes [14, 49, 57, 58, 64]. More recently, ML fairness scholars have argued the current ML system design p”
A Design Space for Effective Privacy Notices (2015, background): “For instance, short notices for smartphone apps have been proposed that convey useful privacy information in the form of risk or expectation scores [53, 79, 80, 88].”
Data colonialism through accumulation by dispossession: New metaphors for daily data (2015, unknown): “Although research has shown these agreements are rarely read (Lin et al., 2012), application creators regularly claim ownership over wide swathes of data not readily apparent to end-users (Bigge, 2006).”
Checking app behavior against app descriptions (2014, result): “In contrast, recent work by Lin et al. [14] suggests crowdsourcing to infer what users expect from speciﬁc privacy settings; just like we found, Lin et al. also highlight that privacy expectations vary between app categories.”
A survey of the use of crowdsourcing in software engineering (2017, methodology): “…software engineering activities, such as software security and privacy analysis (Arellano et al., 2010; Burguera et al., 2011; Sharifi et al., 2011; Lin et al., 2012; Lin, 2013; Agarwal and Hall, 1410 2013; Papamartzivanos et al., 2014; Saxe et al., 2014; Ismail et al., 2015), software end user…”
WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, methodology): “…provided by Felt et al. [28] to highlight the sentences describing the need for a permission, in turn enabling end users to make informed decision while in-stalling and application Finally, Lin et al. [27] introduce a new model for privacy, namely privacy as expectations for mobile applications.”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “Previous research has shown that users often did not understand the installtime permissions dialogs and were often surprised by the data that these over-permissioned apps could access [29, 91, 92].”
Privacy Expectations and Preferences in an IoT World (2017, background): “They found that both individual expectations of what an app does and the purpose for which an app requests access to sensitive resources impacts their privacy decisions [23].”
Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings (2014, methodology): “[13]), collecting over 21,000 privacy preferences associated with different permissions and purposes.”
Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android (2015, background): “Another line of works [20, 3] investigates user perceptions as related to mobile privacy.”