Bibliographic record
Abstract
Every year the amount of digitally stored sensitive information increases significantly. Information such as governmental and legal documents, health, and tax records are required to be securely archived for decades to comply with various laws and regulations. Since cryptographic schemes based on single computational assumptions are not guaranteed to stay secure for such long periods, current state-of-the-art systems providing long-term confidentiality and integrity rely on information-theoretic techniques, such as multi-server secret sharing and commitments. These systems achieve the desired results; however, establishing private channels for secret sharing is costly and requires a complex setup. In this paper, we present ArchiveSafe LT, a framework for archiving systems aiming to provide long-term confidentiality and integrity. The framework relies on multiple computationally-secure schemes using robust combiners, with a design that plans for agility and evolution of cryptographic schemes. ArchiveSafe LT is efficient and suitable for practical adoption as it eliminates the need for private channels compared to its counterparts. We present the ArchiveSafe LT framework structure and its security analysis using an automatic prover. We specify two ArchiveSafe LT-based system designs, which handle different adversarial storage providers. We experimentally evaluate a prototype built based on one of the designs to show the system’s efficiency compared to information-theoretic systems.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".