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Record W2033868240 · doi:10.1109/infocom.2014.6848032

A new efficient physical layer OFDM encryption scheme

2014· article· en· W2033868240 on OpenAlexaff
Fei Huo, Guang Gong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceOrthogonal frequency-division multiplexingWatermarking attackEncryptionOrthogonalityCiphertextDecoding methodsPhysical layerPlaintextScheme (mathematics)Frequency domainAlgorithmTheoretical computer scienceProbabilistic encryptionComputer securityComputer networkChannel (broadcasting)56-bit encryptionMathematicsWirelessTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose a new encryption scheme for OFDM systems. The reason for physical layer approach is that it has the least impact on the system and is the fastest among all layers. This scheme is computationally secure against the adversary. It requires less key streams compared with other approaches. The idea comes from the importance of orthogonality in OFDM symbols. Destroying the orthogonality create intercarrier interferences. This in turn cause higher bit and symbol decoding error rate. The encryption is performed on the time domain OFDM symbols, which is equivalent to performing nonlinear masking in the frequency domain. Various attacks are explored in this paper. These include known plaintext and ciphertext attack, frequency domain attack, time domain attack, statistical attack and random guessing attack. We show our scheme is resistant against these attacks. Finally, simulations are conducted to compare the new scheme with the conventional cipher encryption.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.279
Teacher spread0.263 · 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 designSimulation or modeling
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

Citations31
Published2014
Admission routes1
Has abstractyes

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