Bibliographic record
Abstract
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis,including any required final revisions,as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The traditional model of cryptography examines the security of cryptographic prim-itives as mathematical functions. This approach does not account for the physical side effects of using these primitives in the real world. A more realistic model em-ploys the concept of a side channel. A side channel is a source of information that is inherent to a physical implementation of a primitive. Research done in the last half of the 1990s has shown that the information transmitted by side channels,such as execution time,computational faults and power consumption,can be detrimental to the security of ciphers like DES and RSA. This thesis surveys the techniques of side channel cryptanalysis presented in [30], [10],and [31] and shows how side channel information can be used to break imple-mentations of DES and RSA. Some specific techniques covered include the timing attack,differential fault analysis,simple power analysis and differential power anal-ysis. Possible defenses against each of these side channel attacks are also discussed. iii
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".