Empirical Analysis and Privacy Implications in OAuth-based Single Sign-On Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Single sign-on authentication systems such as OAuth 2.0 are widely used in web services. They allow users to use accounts registered with major identity providers such as Google and Facebook to login to a wide variety of independent services (relying parties). These services can both identify users and access a subset of the user's data stored with the provider. We empirically investigate the end-user privacy implications of OAuth implementations by relying parties around the world. We collect data on the use of OAuth-based logins in the Alexa Top 500 sites per country for five countries. We categorize user data made available by four identity providers (Google, Facebook, Apple, and LinkedIn) and evaluate popular services accessing user data from the SSO platforms of these providers. Many services allow users to choose from multiple login options (with different identity providers). Our results reveal that services request different categories and amounts of personal data from different providers, often with at least one choice undeniably more privacy-intrusive. We find that privacy-friendly login choices tend to be listed last, suggesting a dark pattern favoring options that release more user data. These privacy choices (and their privacy implications) are highly invisible to users. Based on our analysis, we consider challenges (e.g., opposing goals of stakeholders) in addressing these concerns and discuss ideas for further exploration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it