Security Engineering for Large Scale Distributed Applications
Bibliographic record
Abstract
The way security mechanisms for large-scale distributed applications are engineered today has a number of serious drawbacks. As a result, secure distributed applications are a) very expensive and error-prone to build, deploy, and integrate, b) complex and error-prone to operate and administer, and still c) far from being adequate to the real-life problems. Drawing on my academic and industrial experiences, I will discuss several recently invented techniques that can improve engineering of security mechanisms for distributed systems. I will specifically talk about improving those mechanisms that are based on the decision-enforcement paradigm, and will use access control as a representative example. I will examine in detail one particular method, Attribute Function, which enables the use of application-specific data in authorization decisions while keeping distributed applications security unaware. The talkl was given at the following organizations: * Departement Computerwetenschappen, Katholieke Universiteit Leuven, on June 19, 2003. * Department of Electrical and Computer Engineering, University of British Columbia, on March 7, 2003. * The Department of Computing and Software, McMaster University, on February 25, 2003. * Faculty of Computer Science, Dalhousie University, on January 28, 2003.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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".