Mitigator: Privacy policy compliance using trusted hardware
Bibliographic record
Abstract
Abstract Through recent years, much research has been conducted into processing privacy policies and presenting them in ways that are easy for users to understand. However, understanding privacy policies has little utility if the website’s data processing code does not match the privacy policy. Although systems have been proposed to achieve compliance of internal software to access control policies, they assume a large trusted computing base and are not designed to provide a proof of compliance to an end user. We design Mitigator, a system to enforce compliance of a website’s source code with a privacy policy model that addresses these two drawbacks of previous work. We use trusted hardware platforms to provide a guarantee to an end user that their data is only handled by code that is compliant with the privacy policy. Such an end user only needs to trust a small module in the hardware of the remote back-end machine and related libraries but not the entire OS. We also provide a proof-of-concept implementation of Mitigator and evaluate it for its latency. We conclude that it incurs only a small overhead with respect to an unmodified system that does not provide a guarantee of privacy policy compliance to the end user.
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.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".