Privacy Tipping Points in Smartphones Privacy Preferences

Abstract

The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a fourweek study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather information about their personal context and to measure their privacy preferences, by varying app name and the purpose of data collection.Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given.We found that the predominant factor affecting users' choices was the purpose for data access. In our study the purpose varied from being not specified, to vague, to being very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown, participants were more willing to disclose, provided the purpose for requesting the information appeared to be beneficial to them, while participants shared the least when the purpose for data access was solely beneficial to developers.

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

“…Finally, the decision, contextual information, and whether the decision was manual or automatic are recorded in the SmarPer database (9). Optionally, the user can review recent automatic decisions and correct them if they are wrong (10). This user feedback is also incorporated in the model (11 and 12).…”

“…For example, they do not include runtime permission decisions [8], [30]. Other data sets include runtime decisions but were collected in non-realistic scenarios [2], [10]. Initially, we considered using the data set from Wijesekera et al [2], as it seemed to match our requirements.…”

“…These numbers show the effectiveness of our rate-limiting mechanisms (Section IV-D). For comparison, participants in [10] were prompted at least 10 times a day and each prompt required 2-5 minutes to complete. We observe that the distribution of decision types varies considerably across participants, indicating the unique privacy preferences of each user and hinting at the difficulty of predicting permission decisions.…”

“…Each participant used SmarPer (in fully manual mode) for a period of at least 10 days. Unlike previous studies [2], [10], ours relies on decision data collected in real conditions, i.e., participants using SmarPer daily on their own or loaned devices with their own apps. In total, we collected 8,521 unique permission decisions, along with 32 raw contextual features per decision (e.g., time, location, app name, etc.).…”

Opportunities and Challenges Around a Tool for Social and Public Web Activity Tracking

“…There is a great deal of research on designing for privacy in technical systems [14], such as ubiquitous computing systems [10], mobile apps [26], location-based services [28], and more. Work related to privacy and web browsing primarily focus on restricting tracking or alerting users [7], or on how to share browsing data, but only to researchers [21].…”