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Record W2474404021

New results on secure message transmission

2012· article· en· W2474404021 on OpenAlexaff
Reihaneh Alsadat Safavi-Naeini, Mohammed Ashraful Alam Tuhin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCommunication sourceComputer scienceAdversaryComputer networkCommunication complexityDisjoint setsTransmission (telecommunications)Adversary modelReliability (semiconductor)Protocol (science)Upper and lower boundsSet (abstract data type)Theoretical computer scienceMathematicsComputer securityPower (physics)Discrete mathematics
DOInot available

Abstract

fetched live from OpenAlex

In the Secure Message Transmission (SMT) problem two nodes in a network want to communicate securely (both privately and reliably), given that some of the nodes in the network are corrupted by an adaptive byzantine adversary with unlimited computational power. A SMT protocol uses multiple paths between the sender S and a receiver R to guarantee privacy and reliability of the message transmission. Privacy means that the adversary learns no information about the transmitted secret, whereas reliability means that the receiver always receives the message sent by the sender. S and R are connected by n disjoint paths where a subset of which (at most t in case of a threshold adversary) can be corrupted by an adversary. Efficiency parameters of a SMT protocol are the number of rounds (number of interactions between S and R ), communication complexity, and computational complexity. An (e, δ)-SMT protocol bounds the adversary's success probability of breaking privacy and reliability to e and δ, respectively. A SMT protocol for a given number of rounds is optimal if its transmission rate (amount of communication per one bit of message) matches the lower bound for that number of rounds. Optimal protocols have been constructed for a restricted set of parameters. It has been proved that secure SMT is possible if and only if n ≥ 2t + 1, where t is the number of paths corrupted by the adversary. This thesis describes a number of contributions to the study of SMT problem. First, we improve the system parameter of a previously introduced wire virtualization method for constructing optimal protocols using two component protocols. Using the improved wire virtualization method we present the first optimal 1-round (0, δ)-SMT protocol for higher connectivity. Then we introduce a modular approach for constructing SMT protocols using two or more modules. Using this approach we design an optimal 1-round (0, δ)-SMT protocol for the minimum connectivity, which has higher reliability than any comparable protocol. We also design a similar protocol for higher connectivity improving the reliability of the protocol presented in the first part of this thesis. Constructing secure and efficient (in communication) SMT protocols against a threshold adversary has been extensively researched. However less is known about SMT problem for a generalized adversary who can corrupt one out of a set of possible subsets. In this part of the thesis, we focus on 1-round (0, δ)-SMT protocols against a generalized adversary. These protocols are especially attractive because of their possible practical applications. Finally, we introduce a new security definition by presenting an alternative privacy definition, which is based on guessing advantage of the adversary, to construct more efficient (in communication) protocols. Our motivation is that if the received message is long enough and has sufficient entropy, then it can be used as a secret key. We give the relationship between the new security definition and the known one, and revisit bounds on connectivity and transmission rates of SMT protocols under the new definition. We also give constructions for a 1-round and a 3-round protocol, secure under the new definition, that are optimal. The contributions made in this thesis add to the research on the secure message transmission problem and show new directions for future research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.263

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 communication0.0000.001
Open science0.0000.000
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 head0.016
GPT teacher head0.250
Teacher spread0.233 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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