Most OAuth service providers, such as Google and Microsoft, offer only a limited range of coarse-grained data access. As a result, third-party OAuth applications often end up accessing more user data than necessary, even if their developers want to minimize data access. We present OAuthHub, a development framework that leverages users’ personal devices as the intermediary controller for OAuth-based data sharing between cloud services. The key innovations of OAuthHub are: (1) the insight that discretionary data access is largely unnecessary for most OAuth apps, which typically only require access at three well-defined moments—during installation, in response to user actions, and at scheduled intervals; (2) a development framework that requires explicit declarations of intended data access and supports the three common access patterns through intermittently available personal devices; and (3) a centralized runtime permission model for managing OAuth access across providers. We evaluated OAuthHub with three real-world apps on both PCs and mobile phones and found that OAuthHub requires moderate changes to the application code and imposes insignificant performance overheads. Our study with 18 developers showed that participants completed programming tasks significantly faster (9.1 vs. 18.0 minutes) with less code (4.7 vs. 15.8 lines) using OAuthHub than conventional OAuth APIs.

In this paper, we describe the design and evaluation of Panopticon, an educational board game that helps data science students learn the skills of designing privacy-sensitive data practices with fun. Panopticon draws inspiration from the classic economics-themed game Monopoly, but re-imagines Monopoly’s financial system as a data economy and requires players to conduct privacy design related activities as they navigate the game board. We used two learning science principles, peer learning and formative feedback, to guide the game design. We evaluated the game through a user study with 36 players (i.e., 12 game sessions) and compared their learning outcomes to a control group (n=36) who learned privacy design through paper content. To measure the learning outcomes, we developed rubrics to quantitatively assess the quality of the privacy designs, covering the level of detail, the technical feasibility, and the empathy for stakeholders. Our results suggest that Panopticon increased the learning outcomes by 354%, with significant improvements in all three dimensions. Participants also reported it as an entertaining way to learn in the post-study interview.