Efficient enforcement of dynamic cryptographic access control policies for outsourced data
Bibliographic record
Abstract
Outsourcing of their data to third-party service providers is a cost-effective data management strategy for many organizations. Outsourcing, however, introduces new challenges with respect to ensuring the security and the privacy of the data. In addition to the need for standard access control policies, organizations must now be concerned with the privacy of their data and so hiding the data from the service provider is important. Simply encrypting the data before it is transmitted to the service provider is inefficient and vulnerable to security attacks when the access control policies change. Approaches based on two layers of encryption alleviate the privacy concern but still require re-encryption of the data when policies change. This paper presents a novel and efficient solution that employs two layers of encryption of the data and an encrypted data object containing the second access key. Changes to the access control policies are handled by re-encrypting the object containing the affected key, which is an efficient operation. The paper presents our key management approach, a security analysis of our approach, and an evaluation of the performance of a proof of concept implementation of our approach.
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 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 | 0.000 |
| Open science | 0.004 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".