Bibliographic record
Abstract
Attackers can get physical control of a computer in sleep (S3/suspend-to-RAM), if it is lost, stolen, or the owner is being coerced. High-value memory-resident secrets, including disk encryption keys, and private signature/encryption keys for PGP, may be extracted (e.g., via cold-boot or DMA attacks), by physically accessing such a computer. Our goal is to alleviate threats of extracting secrets from a computer in sleep, without relying on an Internet-facing service. We propose Hypnoguard to protect all memory-resident OS/user data across S3 suspensions, by first performing an in-place full memory encryption before entering sleep, and then restoring the plaintext content at wakeup-time through an environment-bound, password-based authentication process. The memory encryption key is effectively "sealed" in a Trusted Platform Module (TPM) chip with the measurement of the execution environment supported by CPU's trusted execution mode (e.g., Intel TXT, AMD-V/SVM). Password guessing within Hypnoguard may cause the memory content to be permanently inaccessible, while guessing without Hypnoguard is equivalent to brute-forcing a high-entropy key (due to TPM protection). We achieved full memory encryption/decryption in less than a second on a mainstream computer (Intel i7-4771 CPU with 8GB RAM, taking advantage of multi-core processing and AES-NI), an apparently acceptable delay for sleep-wake transitions. To the best of our knowledge, Hypnoguard provides the first wakeup-time secure environment for authentication and key unlocking, without requiring per-application changes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.109 | 0.053 |
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".