A Framework for Self-Protecting Cryptographic Key Management
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
Demands to match security with performance in Web applications where access to shared data needs to be controlled dynamically make self-protecting security schemes attractive. Yet, standard schemes focus primarily on correctness as opposed to adaptability and so need to be extended to handle these new scenarios. One of the approaches to enforcing cryptographically controlled access to shared data is to encrypt it with a single secret key that is then distributed to the users requiring access. Data security is ensured by replacing the group key and re-encrypting the affected data whenever group membership changes. Thus, key management (KM) is expensive when changes in group membership occur frequently and involve large amounts of data. This paper presents a framework, based on the autonomic computing paradigm, that allows a KM scheme to continually monitor the rate at which changes in group membership occur and generate keys as well as encrypted replicas to anticipate future changes. Since the keys and encrypted data are generated by anticipation rather than on demand, the long-term cost of KM is minimized. A prototype implementation and experiments showing performance improvements demonstrate the effectiveness of the proposed framework.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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