Secure Processors Part I: Background, Taxonomy for Secure Enclaves and Intel SGX Architecture
Bibliographic record
Abstract
This manuscript is the first in a two part survey and analysis of the state of the art in secure processor systems, with a specific focus on remote software attestation and software isolation. This manuscript first examines the relevant concepts in computer architecture and cryptography, and then surveys attack vectors and existing processor systems claiming security for remote computation and/or software isolation. This work examines in detail the modern isolation container (enclave) primitive as a means to minimize trusted software given practical trusted hardware and reasonable performance overhead. Specifically, this work examines in detail the programming model and software design considerations of Intel’s Software Guard Extensions (SGX), as it is an available and documented enclave-capable system. Part II of this work is a deep dive into the implementation and security evaluation of two modern enclave-capable secure processor systems: SGX and MIT’s Sanctum. The complex but insufficient threat model employed by SGX motivates Sanctum, which achieves stronger security guarantees under software attacks with an equivalent programming model. This work advocates a principled, transparent, and well-scrutinized approach to secure system design, and argues that practical guarantees of privacy and integrity for remote computation are achievable at a reasonable design cost and performance 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".