Adaptive Restructuring of Merkle and Verkle Trees for Enhanced Blockchain Scalability

• URL: http://arxiv.org/abs/2403.00406v1
• Date: Fri, 1 Mar 2024 09:52:50 GMT
• Title: Adaptive Restructuring of Merkle and Verkle Trees for Enhanced Blockchain Scalability
• Abstract summary: We propose an adaptive restructuring of Merkle and Verkle trees, fundamental components of blockchain architecture. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns. This study's implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification.
• Score: 28.727306785987537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scalability of blockchain technology remains a pivotal challenge, impeding its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of Merkle and Verkle trees, fundamental components of blockchain architecture responsible for ensuring data integrity and facilitating efficient verification processes. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns, significantly reducing the average path length required for verification and, consequently, the computational overhead associated with these processes. Through a comprehensive conceptual framework, we delineate the methodology for adaptive restructuring, encompassing both binary and non-binary tree configurations. This framework is validated through a series of detailed examples, demonstrating the practical feasibility and the efficiency gains achievable with our approach. Moreover, we present a comparative analysis with existing scalability solutions, highlighting the unique advantages of adaptive restructuring in terms of simplicity, security, and efficiency enhancement without introducing additional complexities or dependencies. This study's implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification that could facilitate broader adoption of blockchain technology in finance, supply chain management, and beyond. As the blockchain ecosystem continues to evolve, the principles and methodologies outlined herein are poised to contribute significantly to its growth and maturity.

Related papers

• Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy [57.058369251551795]
  Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.<n>We propose SCISE, a framework that preserves structural Integrity by synergizing community-aware sampling with constrained Structural Entropy.<n>We show that SCISE significantly outperforms state-of-the-art algorithms, with ablation studies and robustness analyses further validating its effectiveness and reliability for real-world large-scale graphs.
  arXiv  Detail & Related papers  (2026-07-06T07:55:53Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning [3.189189590825304]
  This paper introduces Resilient Federated Chain (RFC), a novel blockchain-enabled Federated Learning framework.<n> RFC builds upon the existing Proof of Federated Learning architecture by repurposing the redundancy of its Pooled Mining mechanism.<n> RFC significantly improves robustness compared to baseline methods, providing a viable solution for securing decentralized learning environments.
  arXiv  Detail & Related papers  (2026-02-25T12:20:47Z)
• AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation [8.765438402697892]
  Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies.<n>We present AdvSynGNN, a comprehensive architecture designed for resilient node-level representation learning.
  arXiv  Detail & Related papers  (2026-02-19T04:26:57Z)
• Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning [58.533203990515034]
  Scaling neural networks has driven breakthrough advances in machine learning, yet this paradigm fails in deep reinforcement learning (DRL)<n>We show that dynamic sparse training strategies provide module-specific benefits that complement the primary scalability foundation established by architectural improvements.<n>We finally distill these insights into Module-Specific Training (MST), a practical framework that exploits the benefits of architectural improvements and demonstrates substantial scalability gains across diverse RL algorithms without algorithmic modifications.
  arXiv  Detail & Related papers  (2025-10-14T03:03:08Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach [55.861432910722186]
  UniToCom is a unified token communication paradigm that treats tokens as the fundamental units for both processing and wireless transmission.<n>We propose a generative information bottleneck (GenIB) principle, which facilitates the learning of tokens that preserve essential information.<n>We employ a causal Transformer-based multimodal large language model (MLLM) at the receiver to unify the processing of both discrete and continuous tokens.
  arXiv  Detail & Related papers  (2025-07-02T14:03:01Z)
• TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree [52.44403214958304]
  In this paper, we introduce TreeLoRA, a novel approach that constructs layer-wise adapters by leveraging hierarchical gradient similarity.<n>To reduce the computational burden of task similarity estimation, we employ bandit techniques to develop an algorithm based on lower confidence bounds.<n> experiments on both vision transformers (ViTs) and large language models (LLMs) demonstrate the effectiveness and efficiency of our approach.
  arXiv  Detail & Related papers  (2025-06-12T05:25:35Z)
• Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection [0.7510165488300369]
  We propose a dynamic feature fusion model that combines graph-based representation learning and semantic feature extraction for fraud detection. We develop a comprehensive data processing pipeline, including graph construction, temporal feature enhancement, and text preprocessing. Experimental results on large-scale real-world blockchain datasets demonstrate that our method outperforms existing benchmarks across accuracy, F1 score, and recall metrics.
  arXiv  Detail & Related papers  (2025-01-03T09:04:43Z)
• Structural Entropy Guided Probabilistic Coding [52.01765333755793]
  We propose a novel structural entropy-guided probabilistic coding model, named SEPC. We incorporate the relationship between latent variables into the optimization by proposing a structural entropy regularization loss. Experimental results across 12 natural language understanding tasks, including both classification and regression tasks, demonstrate the superior performance of SEPC.
  arXiv  Detail & Related papers  (2024-12-12T00:37:53Z)
• AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation [48.82264764771652]
  We introduce AsCAN -- a hybrid architecture, combining both convolutional and transformer blocks. AsCAN supports a variety of tasks: recognition, segmentation, class-conditional image generation. We then scale the same architecture to solve a large-scale text-to-image task and show state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-07T18:43:17Z)
• Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach [20.821562115822182]
  This paper introduces a novel OR-aggregation approach for zero-knowledge set membership proofs. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Results show significant improvements in proof size, generation time, and verification efficiency.
  arXiv  Detail & Related papers  (2024-10-11T18:16:34Z)
• Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation [2.28438857884398]
  This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
  arXiv  Detail & Related papers  (2024-03-05T20:54:56Z)
• BlockFUL: Enabling Unlearning in Blockchained Federated Learning [26.47424619448623]
  Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. In this paper, we introduce a novel framework with a dual-chain structure comprising a live chain and an archive chain for enabling unlearning capabilities withined FL. Two new unlearning paradigms, i.e., parallel and sequential paradigms, can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. Our experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL.
  arXiv  Detail & Related papers  (2024-02-26T04:31:53Z)
• Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms [15.034624246970154]
  Current approaches to data verification in blockchain systems face challenges in terms of efficiency and computational overhead. This study proposes an innovative aggregation scheme for Zero-Knowledge Proofs within the structure of Merkle Trees. We develop a system that significantly reduces the size of the proof and the computational resources needed for its generation and verification.
  arXiv  Detail & Related papers  (2024-02-06T09:26:46Z)
• A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution [50.13564338607482]
  We propose a novel optimization framework for the hyperspectral deconvolution problem, called DeepMix.<n>It consists of three distinct modules, namely, a data consistency module, a module that enforces the effect of the handcrafted regularizers, and a denoising module.<n>This work proposes a context aware denoising module designed to sustain the advancements achieved by the cooperative efforts of the other modules.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer [95.71132572688143]
  This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency.
  arXiv  Detail & Related papers  (2023-04-12T07:34:13Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
  offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
  arXiv  Detail & Related papers  (2022-02-19T20:22:04Z)
• Secure and Efficient Federated Learning Through Layering and Sharding Blockchain [15.197940168865271]
  This paper proposes ChainFL, a novel two-layer blockchain-driven Federated Learning system. It splits the Internet network into multiple shards within the subchain layer, effectively reducing the scale of information exchange. It also employs a Direct Acyclic Graph (DAG)-based mainchain as the mainchain layer, enabling parallel and asynchronous cross-shard validation.
  arXiv  Detail & Related papers  (2021-04-27T12:19:07Z)
• Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives [73.15276998621582]
  We propose a generic feature learning mechanism to advance CNN training with enhanced generalization ability. Partially inspired by DSN, we fork delicately designed side branches from the intermediate layers of a given neural network. Experiments on both category and instance recognition tasks demonstrate the substantial improvements of our proposed method.
  arXiv  Detail & Related papers  (2020-03-24T09:56:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.