A Framework for Self-Protecting Cryptographic Key Management
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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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".