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Record W2132982295 · doi:10.1109/iscas.2008.4542069

Compact ASIC Implementation of the ICEBERG Block Cipher with Concurrent Error Detection

2008· article· en· W2132982295 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 institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsApplication-specific integrated circuitBlock cipherComputer scienceEmbedded systemField-programmable gate arrayError detection and correctionOverhead (engineering)Fault detection and isolationCipherCMOSThroughputBlock (permutation group theory)Computer hardwareEncryptionEngineeringComputer networkOperating systemAlgorithmElectronic engineering

Abstract

fetched live from OpenAlex

ICEBERG is a block cipher that has been recently proposed for security applications requiring efficient FPGA implementations. In this paper, we investigate a compact ASIC implementation of ICEBERG and consider the novel application of concurrent error detection to protect the implementation from fault-based attacks. The compact architecture of ICEBERG requires about 5800 gates with a throughput of 552 Mbps in an ASIC implementation based on 0.18 μm CMOS technology. The addition of an effective multiple parity concurrent error detection scheme to protect the hardware from fault attacks results in a 62% area overhead.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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