We introduce the Huffman-Merkle Tree (HMT), an authenticated data structure (ADS) for dynamic workloads where items may differ in access frequencies, and access frequencies can change over time. An ADS allows proving item membership against a short commitment to a large mutable state, with applications including verifiable storage, Internet transparency services, and blockchains. Optimizing ADS performance under continuously changing access frequencies has not been fully addressed before, neither in theory nor in practice. HMT addresses dynamically changing access skew through two complementary mechanisms. The first is a Huffman-coding-based Merkle-tree layout, with a novel extension to support evolving access frequencies. The second is an elastic tiering regime that partitions items across separate trees, such as hot and cold tiers, with adaptive migration between them. The key insight in this approach is to place frequently accessed items closer to the root, while assigning less frequently accessed items to progressively larger and deeper trees. This reduces the overall frequency-weighted access cost. Our scheme is designed to scale to gigabytes of data spanning millions of items. To handle dynamism efficiently, layout updates are applied in batches, access frequencies are tracked using a count-min sketch, and the system employs a tier-promotion cache while exploring multiple tier-migration policies. We implement HMT and compare it on real-world data with Ethereum's Merkle Patricia Trie (MPT) ADS and its proposed replacement, the Unified Binary Tree (UBT). Our evaluation considers two metrics: the amount of hashing per update and access-weighted membership-proof size. The latter captures both item access cost and frequency. We find that the best HMT policy uses about 2.4x and 0.34x less average hash operations than MPT and UBT respectively, and has 0.18x and 0.55x shorter proofs.

翻译：暂无翻译