Compact Hardware Implementation of the Block Cipher Camellia with Concurrent Error Detection
Bibliographic record
Abstract
A compact hardware implementation of a block cipher is attractive for any low-cost embedded application like smart cards. In this paper, a compact hardware architecture for Camellia is investigated. In this architecture, encryption and key scheduling share the same datapath and a four s-box iterative structure is employed. In the hardware design of cryptographic algorithms, concurrent error detection (CED) techniques have been proposed not only to protect the encryption and decryption process from random faults but also from the intentionally injected faults by some attackers. In our design, we also investigate a multiple parity code based error detection scheme. In our CED scheme, all the components are protected and all single-bit faults and most multiple faults will be detected. We study the implementation of the compact architecture for an ASIC and an FPGA. The design requires 14.12K gates with a throughput of 143 Mbps based on 0.18-um CMOS standard cell library and 1052 slices with a throughput of 135 Mbps based on Xilinx Virtex-E v1000efg860 chip. For our concurrent error detection, the hardware overhead is about 83%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".