Access control to people location information
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
Ubiquitous computing uses a variety of information for which access needs to be controlled. For instance, a person's current location is a sensitive piece of information that only authorized entities should be able to learn. Several challenges arise in the specification and implementation of policies controlling access to location information. For example, there can be multiple sources of location information. The sources can be within different administrative domains, which might allow different entities to specify policies, and policies need to be flexible. We address these issues in our design of a distributed access control mechanism for a people location system. Our design encodes policies as digital certificates, which enables decentralized storage of policies. We also present an algorithm for the discovery of distributed certificates. Furthermore, we discuss several privacy issues and show how our design addresses them. To show feasibility of our design, we built an example implementation based on SPKI/SDSI certificates. Using measurements, we quantify the influence of access control on query processing time. We also discuss trade-offs between RSA-based and DSA-based signature schemes for digital certificates.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it