Preventing Sybil Attacks by Privilege Attenuation: A Design Principle for Social Network Systems
Bibliographic record
Abstract
In Face book-style Social Network Systems (FSNSs), which are a generalization of the access control model of Face book, an access control policy specifies a graph-theoretic relationship between the resource owner and resource access or that must hold in the social graph in order for access to be granted. Pseudonymous identities may collude to alter the topology of the social graph and gain access that would otherwise be forbidden. We formalize Denning's Principle of Privilege Attenuation (POPA) as a run-time property, and demonstrate that it is a necessary and sufficient condition for preventing the above form of Sybil attacks. A static policy analysis is then devised for verifying that an FSNS is POPA compliant (and thus Sybil free). The static analysis is proven to be both sound and complete. We also extend our analysis to cover a peculiar feature of FSNS, namely, what Fong et al. dubbed as Stage-I Authorization. We discuss the anomalies resulted from this extension, and point out the need to redesign Stage-I Authorization to support a rational POPA-compliance analysis.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".