Now You See It, Now You Don’t: Obfuscation of Online Third-Party Information Sharing
Bibliographic record
Abstract
The practice of sharing online user information with external third parties has become the focal point of privacy concerns for consumer advocacy groups and policy makers. We explore the decisions by websites regarding the obfuscation that they use to make it difficult for users to discover the extent of information sharing. Using a Bayesian model, we shed light on the websites’ incentive to obfuscate user information sharing. We find that as content sensitivity increases, a website reduces its level of obfuscation. Furthermore, more popular websites engage in higher levels of obfuscation than less popular ones. We provide an empirical analysis of obfuscation and user information sharing in News (low content sensitivity) and Health (high content sensitivity) websites and confirm key results from our analytical model. Our analysis illustrates that obfuscation of information sharing is a viable strategy that websites use to improve their profits. History: Ram Ramesh, area editor for Data Science & Machine Learning. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1266 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0070 ) at http://dx.doi.org/10.5281/zenodo.7336098 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".