Compact Hardware Implementation of the Block Cipher Camellia with Concurrent Error Detection
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.
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%.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it