Privacy policy enforcement in enterprises with identity management solutions
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.
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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".