Bidirectional Privacy Preservation in Web Services
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
In web-based services, users are often required to submit personal data, which may be shared with third parties. Although privacy regulations mandate the disclosure of intended recipients in privacy policies, this does not fully alleviate users’ privacy concerns. The presence of a privacy policy does not ensure compliance, since users must assess the trustworthiness of all parties involved in data sharing. On the other hand, service providers want to minimize the costs associated with preserving user privacy. Indeed, service providers may have their own privacy preservation requirements, such as hiding the identities of third-party suppliers. We present a novel framework designed to tackle the dual challenges of bidirectional privacy preservation and cost-effectiveness. Our framework safeguards the privacy of service users, providers, and various layers of intermediaries in data-sharing environments, while also reducing the costs incurred by service providers related to data privacy. This combination makes our solution a practical choice for web services. We have implemented our solution and conducted a performance analysis to demonstrate its viability. Additionally, we prove its privacy and security within a Universal Composability (UC) framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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