Techniques of Side Channel Cryptanalysis
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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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