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Mobile devices have access to personal, potentially sensitive data, and thereis a large number of mobile applications and third-party libraries thattransmit this information over the network to remote servers (including appdeveloper servers and third party servers). In this paper, we are interested inbetter understanding of not just the extent of personally identifiableinformation (PII) exposure, but also its context i.e., functionality of theapp, destination server, encryption used, etc.) and the risk perceived bymobile users today. To that end we take two steps. First, we perform ameasurement study: we collect a new dataset via manual and automatic testingand capture the exposure of 16 PII types from 400 most popular Android apps. Weanalyze these exposures and provide insights into the extent and patterns ofmobile apps sharing PII, which can be later used for prediction and prevention.Second, we perform a user study with 220 participants on Amazon MechanicalTurk: we summarize the results of the measurement study in categories, presentthem in a realistic context, and assess users' understanding, concern, andwillingness to take action. To the best of our knowledge, our user study is thefirst to collect and analyze user input in such fine granularity and on actual(not just potential or permitted) privacy exposures on mobile devices. Althoughmany users did not initially understand the full implications of their PIIbeing exposed, after being better informed through the study, they becameappreciative and interested in better privacy practices.

Exposures Exposed: A Measurement and User Study to Assess Mobile Data Privacy in Context