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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.243

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 communication0.0000.000
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 head0.015
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