MétaCan
Menu
Back to cohort

Information-theoretic Key Encapsulation and its Application to Secure Communication

2021· preprint· en· W3127318453 on OpenAlexaff
Setareh Sharifian, Reihaneh Safavi–Naini

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKey encapsulationComputer scienceEncryptionPlaintext40-bit encryptionTheoretical computer sciencePre-shared keyProbabilistic encryptionEncapsulation (networking)Symmetric-key algorithmHybrid cryptosystemAlice and BobPublic-key cryptographyCiphertextPseudorandom number generatorKey distributionComputer securityAlgorithmAlice (programming language)

Abstract

fetched live from OpenAlex

A hybrid encryption scheme consists of a public-key part called the key encapsulation mechanism (KEM), that is used to generate and establish a shared secret key between two parties, and a (symmetric) secret-key part called the data encapsulation mechanism (DEM) that encrypts the data using the shared key. Hybrid encryption schemes are widely used for securing Internet communication. In this paper, we initiate the study of hybrid encryption in preprocessing model which assumes access to initial correlated variables by all parties (including the eavesdropper), and define information-theoretic KEM (iKEM) that together with a (computationally) secure DEM, results in a hybrid encryption scheme in preprocessing model. We define security of each building block, and prove a composition theorem that guarantees (computational) q-chosen-plaintext attack (CPA) security of the hybrid encryption system if the iKEM and the DEM satisfy q-chosen-encapsulation attack security and one-time security, respectively. We show that an iKEM can be realized by an information-theoretic one-way secret key agreement (OW-SKA) protocol where a single message is transmitted from Alice to Bob, with a new security definition that allows <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> queries to Alice. Using a OW-SKA that satisfies this new definition of security, effectively allows the established secret key to be used with a onetime symmetric key encryption system that can be implemented, for example, by XORing the output of a (computationally) secure pseudorandom generator with the message, to provide secure encryption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> arbitrary messages (polynomially bounded length). We discuss our results and directions for future work.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.237
Teacher spread0.229 · 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 teacher head, not a consensus.

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

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicCryptography and Data SecurityFrench-language works237,207