Privacy policy enforcement in enterprises with identity management solutions
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
People are usually asked by enterprises to disclose their personal information to access web services and engage in business interactions. Enterprises need this information to enable their business processes. This is unlikely to change, at least in the foreseeable future. When collecting personal d ata, enterprises must satisfy privacy laws and policies along with addressing people's expectations on how their data should be handled. Currently much is done by means of manual processes, in particular in terms of privacy enforcement: these processes are prone to mistakes and hard to comply with. Automation can help enterprises to deal with these privacy management issues, in particular the enforcement of privacy policies on collected personal data. Enterprises have already been investing in identity management solutions: they require that approaches to automate privacy management should keep into account and leverage these solutions. This paper discusses our research and development work to automate the enforcement of privacy policies in enterprises. Our model of privacy policy enforcement is introduced along with the technical details of a related prototype, integrated (as a proof of concept) with HP Select Access, a state-of-the-art identity management solution. This technology is currently under productisation. We discuss our current results and next steps.
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 | 0.000 |
| 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