The design of a secure agent platform
Bibliographic record
Abstract
This paper contains a description of the enhancement of TEEMA, an extensible, general-purpose mobile agent execution environment, to support secure communications. Software agents are proactive, autonomous programs that are given the option to migrate among supporting platforms. Agents can mimic the behavior of a number of systems, from simple cellular automata to complex handoff sequences in cellular systems. They can also be adopted to represent users and act on their behalf in such environments as economic commerce and online communities. To be used in this capacity, agents must be trusted by users. Unfortunately, agents must often rely on the good faith of the execution environments and must be trusted within any environment they are allowed to reside. This poses a serious security threat when agents, for example, need to establish a transaction that involves the exchange of sensitive information, like, for example, credit card numbers, social insurance numbers, but also telephones, medical information, and other sensitive data. The obvious solution to this problem is to make the channels transmitting such sensitive information safe from tampering and interception. The construction of a secure system starts by building a secure environment for the execution environments. TEEMA is written completely in Java, and this makes it possible to adopt existing security protocols.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".