Self-Stabilizing Secure Computation
Bibliographic record
Abstract
Self-stabilization refers to the ability of systems to recover after temporal violations of conditions required for their correct operation. Such violations may lead the system to an arbitrary state from which it should automatically recover. Typically, a self-stabilizing algorithm is examined for eventual functionality, namely, whether the algorithm eventually exhibit the desired input output relation. In this article, we extend the typical functionality criteria to include the recovery of privacy and security aspects. In cryptographic protocol problems, two or more parties want to perform some joint computation, while guaranteeing security properties against adversarial behavior. Current cryptographic protocols guarantee these security properties as long as the adversary is limited to compromise only a fraction of the parties. However, in reality, the adversary may compromise all the parties of the system for a while. We introduce the notion of Self-Stabilizing Secure Computation, a design that ensures that the security properties of computation are automatically regained, even if at some point the entire system is compromised. We then propose a self-stabilizing secure protocol for the evaluation of a reactive functionality which yields a computation of a virtual global finite state machine.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".