Batching Anonymous and Non-Anonymous Membership Proofs for Blockchain Applications
Bibliographic record
Abstract
Membership proof is a very useful building block for checking if an entity is in a list. This tool is widely used in many scenarios. For instance in blockchain where checking membership of an unspent coin in a huge set is necessary, or in the scenario where certain privacy-preserving property on the list or on the entity is required. When it comes to multi-user applications, the naive way that verifies the membership relations one by one is very inefficient. In this work, we utilize subvector commitment schemes and non-interactive proofs of knowledge of elliptic curve discrete logarithms to present two batched membership proofs for multiple users, i.e., batched non-anonymous membership proofs and batched anonymous membership proofs, which offer plausible anonymity assurance respectively on the organization group list and on the users when combined within the blockchain applications. The non-anonymous membership proof scheme requires a trusted setup, but its proof size is only one bilinear group element and is independent of both the size of list and the number of users. The anonymous membership proof scheme requires no trusted setup, and its proof size is linear in the size of organization group and is independent of the number of users. Their security relies respectively on the CubeDH and the discrete logarithm assumptions. Finally, as a use-case application scenario, we extend Mesh which is a blockchain based supply chain management solution to Mesh+ which supports batched anonymous membership proofs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".