Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2608.25206

Authenticated Data Structures for Dynamic Workloads

Ziheng Shangguan·Aviv Yaish·Dahlia Malkhi
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ziheng Shangguan, Aviv Yaish, Dahlia Malkhi. 2026-08-25. Authenticated Data Structures for Dynamic Workloads. https://arxiv.org/abs/2608.25206

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗
arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗
arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗