Riding on Asymmetry: Efficient ABE for Branching Programs.
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
In an Attribute-Based Encryption (ABE) a ciphertext, encrypting message µ, is associated with a public attribute vector x and a secret key skP is associated with a predicate P. The decryption returns µ if and only if P (x) = 1. ABE provides efficient and simple mechanism for data sharing supporting fine-grained access control. Moreover, it is used as a critical component in constructions of succinct functional encryption, reusable garbled circuits, token-based obfuscation and more. In this work, we describe a new efficient ABE scheme for a family of branching programs with short secret keys over a small ring. In particular, in our constriction the size of the secret key for a branching program P is |P |+poly(λ), where λ is the security parameter. Our construction is secure assuming nω(1)-hardness of standard Learning With Errors (LWE) problem, resulting in small ring modulo. Previous constructions relied on nO(logn)-hardness of LWE (resulting in large ring modulo) or had large secret keys of size |P |×poly(λ). We rely on techniques developed by Boneh et al. (EUROCRYPT’14) and Brakerski et al. (ITCS’14) in the context of ABE for circuits and fully-homomorphic encryption.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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