USENIX Security2022Top-tier venue
Hyperproofs: Aggregating and Maintaining Proofs in Vector Commitments
Shravan Srinivasan, Alexander Chepurnoy, Charalampos Papamanthou, Alin Tomescu, Yupeng Zhang
Abstract
We present Hyperproofs, the first vector commitment (VC) scheme that is efficiently maintainable and aggregatable. Similar to Merkle proofs, our proofs form a tree that can be efficiently maintained: updating all n proofs in the tree after a single leaf change only requires O(log n) time. Importantly, unlike Merkle proofs, Hyperproofs are efficiently aggregatable, anywhere from 10× to 41× faster than SNARK-based aggregation of Merkle proofs. At the same time, an individual Hyperproof consists of only log n algebraic hashes (e.g., 32-byte elliptic curve points) and an aggregation of b such proofs is only O(log (b log n))-sized. Hyperproofs are also reasonably fast to update when compared to Merkle trees with SNARK-friendly hash functions. As another benefit over Merkle trees, Hyperproofs are homomorphic: digests (and proofs) for two vectors can be homomorphically combined into a digest (and proofs) for their sum. Homomorphism is very useful in emerging applications such as stateless cryptocurrencies. First, it enables unstealability, a novel property that incentivizes proof computation. Second, it makes digests and proofs much more convenient to update. Finally, Hyperproofs have certain limitations: they are not transparent, have linear-sized public parameters, are slower to verify, and have larger aggregated proofs and slower verification than SNARK-based approaches. Nonetheless, end-to-end, aggregation and verification in Hyperproofs is 10× to 41× faster than in SNARK-based Merkle trees. Observations: For simplicity, we give our algorithms oracle access to the public parameters pp of the scheme. This way, each algorithm can easily access the subset of the parameters it needs. We formalize OpenAll and UpdAllProofs since, in some VCs, these algorithms are faster than n calls to Open and UpdProof, respectively. In this sense, we stress that the UpdAllProofs algorithm can work in sublinear time, since it does not necessarily need to read all input or write all output (e.g., in Merkle trees, UpdAllProofs only reads log n sibling hashes and overwrites another log n hashes).
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