Enhanced Chosen-Ciphertext Security and Applications.
Bibliographic record
Abstract
We introduce and study a new notion of enhanced chosen-ciphertext security (ECCA) for publickey encryption. Loosely speaking, in ECCA, when the decryption oracle returns a plaintext to the adversary, it also provides coins under which the returned plaintext encrypts to the queried ciphertext (when they exist). Our results mainly concern the case where such coins can also be recovered efficiently. We provide constructions of ECCA encryption from adaptive trapdoor functions as defined by Kiltz et al. (EUROCRYPT 2010), resulting in ECCA encryption from standard number-theoretic assumptions. We then give two applications of ECCA encryption: (1) We use it as a unifying concept in showing equivalence of adaptive trapdoor functions and tag-based adaptive trapdoor functions (namely, we show that both primitives are equivalent to ECCA encryption), resolving a main open question of Kiltz et al. (2) We show that ECCA encryption can be used to securely realize an approach to public-key encryption with non-interactive opening (PKENO) suggested by Damg˚ard and Thorbek (EUROCRYPT 2007), resulting in new and practical PKENO schemes quite different from those in prior work. We believe our results indicate that ECCA is an intriguing notion that may prove useful in further work.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".