Computational Soundness of Coinductive Symbolic Security under Active Attacks.
Bibliographic record
Abstract
Abstract. In Eurocrypt 2010, Miccinacio initiated an investigation of cryptographically sound, symbolic security analysis with respect to coinductive adversarial knowledge, and demonstrated that under an adversarially passive model, certain security criteria (e.g. indistinguishability) may be given a computationally sound symbolic characterization, without the assumption of key acyclicity. Left open in his work was the fundamental question of “the viability of extending the coinductive approach to prove computational soundness results in the presence of active adversaries.” In this paper we make some initial steps toward answering this question in the affirmative with respect to an extension of a trace-based security model (proposed by Micciancio and Warinschi in TCC 2004) including asymmetric and symmetric encryption; in particular we prove that a random computational trace can be soundly abstracted by a coinductive symbolic trace with overwhelming probability, provided that both the underlying encryption schemes provide IND-CCA2 security (plus ciphertext integrity for the symmetric scheme), and that the diameter of the underlying coinductively-hidden subgraph is constant in every symbolic trace. This result holds even if the protocol allows arbitrarily nested applications of symmetric/asymmetric encryption, unrestricted transmission of symmetric keys, and adversaries who adaptively corrupt users, along with other forms of active attack. As part of our proof, we formulate a game-based definition of encryption security allowing adaptive corruptions of keys and certain forms of adaptive key-dependent plaintext attack, along with other common forms of CCA2 attack. We prove that (with assumptions similar to above,) security under this game is implied by IND-CCA2 security. This also characterizes a provably benign form of cyclic encryption which can be achieved under standard notions of encryption security, which may be of independent interest.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".