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Record W3029610118

Computational Soundness of Coinductive Symbolic Security under Active Attacks.

2012· preprint· en· W3029610118 on OpenAlexaff
Mohammad Hajiabadi, Bruce M. Kapron

Bibliographic record

VenueIACR Cryptology ePrint Archive · 2012
Typepreprint
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSoundnessComputer scienceEncryptionTRACE (psycholinguistics)Theoretical computer scienceSymmetric-key algorithmCiphertextMathematicsComputer securityPublic-key cryptographyProgramming language
DOInot available

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0020.014
Scholarly communication0.0050.014
Open science0.0030.007
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.024
GPT teacher head0.289
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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