ALLOSAUR: Accumulator with Low-Latency Oblivious Sublinear Anonymous credential Updates with Revocations
Bibliographic record
Abstract
A cryptographic accumulator is a compressed data structure with associated algorithms used for secure membership testing. In the growing space of digital credentials, accumulators are used to manage sets of valid credentials, giving efficient and anonymous methods for credential holders to prove their validity. Unlike traditional credentials like digital signatures, one can easily revoke credentials with an accumulator; however, each revocation forces existing credential holders to engage in an expensive update process. Previous works make this faster and easier by sacrificing anonymity. To improve performance without compromising privacy, we present ALLOSAUR, a multi-party accumulator based on pairings. In ALLOSAUR, we eliminate the cost of accumulating new credentials, let "credential managers" manage the accumulator values with secure multiparty computation, and allow anonymous credential updates with a square-root reduction in communication costs as compared to existing work.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".