最新区块链论文速读--CCF C会议 ISPA 2023 共11篇 附pdf下载 （2/2）_ispa会议论文

Conference：21st IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2023)

CCF level：CCF C

Categories：Computer Architecture/Parallel and Distributed Computing/Storage Systems

Year：2023

Num：11

Conference time：21-24 December, 2023

第1~6篇区块链文章 请点击此处查看

7

Title: 

Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE

基于区块链与TEE的云端数据操作行为可信审计

Authors: 

Key words:

Behavior audit of data operation, Blockchain, Privacy protection

数据操作行为审计、区块链、隐私保护

Abstract: 

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users’ identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible.

随着云计算、5G、物联网等新一代信息技术的快速发展，数据外包存储于云端为随时随地的数据存储或访问带来了极大的便利。由于外包存储数据所有权和管理权分离，面临数据丢失、个人隐私泄露等数据安全风险。为加强外包数据的安全性，通常采用第三方通过日志对数据操作行为进行审计。然而，近年来发生的一些隐私泄露事件表明，可能泄露数据和用户身份信息的第三方并不总是值得信赖的。为实现数据安全和隐私保护，该文提出了一种利用区块链而非第三方进行外包数据操作行为审计方案，从操作行为日志的完整性、操作行为信息的可信度、操作行为的合规性三个维度开展数据操作行为审计。安全性分析与实验结果表明该方案安全可行。

Pdf link:

https://ieeexplore.ieee.org/document/10491618

8

Title: 

Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain Systems

区块链系统中基于多分区的交易数据管理优化

Authors: 

Key words:

UTXO, blockchain transaction data set, memory usage, data management, transaction verification

UTXO、区块链交易数据集、内存使用、数据管理、交易验证

Abstract: 

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods.

未花费交易输出（UTXO）是交易数据集的一部分，在基于交易的区块链系统中代表数字加密货币资产。UTXO 的数据管理能力、存储方式和占用空间将极大地影响区块链系统的运行效率和验证性能。特别是随着区块链技术的普及，相关的 UTXO 数据集不断增长，所有存储的数据已经无法几乎完全存储在内存中。此时 UTXO 交易数据应该如何存储和管理，是类比特币区块链系统中亟待解决的问题。本文提出了一种基于多分区的区块链交易数据管理优化机制。首先，我们通过真实的区块链数据分析交易的影响因素。所提出的方法可以评估时间间隔和交易频率因素，并利用接收到的信息实现高效的交易数据存储。在我们的设计中，在新交易中使用可能性较低的 UTXO 将存储在磁盘中，而其他在新生成的交易中使用可能性较高的 UTXO 则应存储在缓存中。该方法旨在最小化交易数据集的内存消耗，加速区块验证过程中的UTXO访问时间，最终减少验证所需的总体时间，实现高效的UTXO交易数据管理。最后，通过理论分析和仿真实验验证了所提优化机制的有效性，与最新方法相比，UXTO访问时间有所减少。

Pdf link:

https://ieeexplore.ieee.org/document/10491600

9

Title: 

A High-Performance Data Verification Mechanism in Blockchain-Based IoT Systems

基于区块链的物联网系统中的高性能数据验证机制

Authors: 

Key words:

IoT systems, data security, blockchain, transaction information, verification efficiency

物联网系统、数据安全、区块链、交易信息、验证效率

Abstract: 

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification.

随着分布式网络与通信的快速发展，物联网系统已应用到各行各业。区块链技术是近年来最热门的研究领域之一，可以为物联网系统提供可靠的存储方案并解决信息安全问题。交易信息是区块链系统中最关键的基础数据，在传输过程中需要进行验证以避免被恶意节点篡改。随着物联网设备数量的指数级增长，当前存储结构的局限性给系统节点带来了过大的压力，如何提高验证效率成为关键挑战。针对这一问题，本文提出了一种基于区块链的物联网系统中数据安全保护的高性能验证机制。设计了一种基于哈夫曼主干树（HTT）的新型存储结构，并对交易权重进行了量化分析。在功能齐全的设备中以哈夫曼默克尔树（HMT）的形式存储交易，在轻量级设备中仅保存HTT的内容。最后通过理论分析和实验评估证明了该机制的性能优越性。在基于区块链的物联网系统中，我们的机制显著降低了数据传输成本和计算开销，有效提高了数据验证的效率。

Pdf link:

https://ieeexplore.ieee.org/document/10491673

10

Title: 

A Space-Efficient Digital Wallet Service in Blockchain Systems

区块链系统中空间高效的数字钱包服务

Authors: 

Key words:

digital wallet, UTXO, simulated annealing algorithm, transaction fees, greedy algorithm

数字钱包，UTXO，模拟退火算法，交易费，贪婪算法

Abstract: 

As an indispensable key network service in blockchain systems, digital wallet service is crucial for promoting the widespread application of blockchain technology and the development of the digital economy. However, with the increasing popularity of blockchain technology, the scale of the Unspent Transaction Output (UTXO) dataset continues to increase. A significant number of low-value UTXOs occupied the main dataset space, leading to blockchain dataset expansion. This has affected the performance of digital wallet service and the overall system performance. To address this issue, this paper proposes a Space-Efficient Digital Wallet (SEDW) service in blockchain systems. This strategy employs a Multidimensional Space Simulated Annealing (MSA) algorithm to obtain transaction input UTXOs. MSA can be combined with the UTXO dynamic adjustment mechanism, achieving the rapid consumption of low-value UTXOs without increasing the transaction fees. Experimental results demonstrate that the proposed SEDW strategy effectively optimized the selection of UTXOs in blockchain systems. It alleviated the expansion issue of the UTXO dataset, and controlled the generation of transaction fees, thereby achieving performance enhancement of digital wallet service.

数字钱包服务作为区块链系统中不可或缺的关键网络服务，对于推动区块链技术的广泛应用和数字经济发展至关重要。然而随着区块链技术的日益普及，未花费交易输出（UTXO）数据集的规模不断增大，大量低价值UTXO占据了主要数据集空间，导致区块链数据集膨胀，影响了数字钱包服务的性能和整体系统性能。针对该问题，本文提出了一种区块链系统中空间高效的数字钱包（SEDW）服务。该策略采用多维空间模拟退火（MSA）算法来获取交易输入UTXO。MSA可与UTXO动态调整机制相结合，在不增加交易费用的情况下实现低价值UTXO的快速消耗。实验结果表明，提出的SEDW策略有效优化了区块链系统中UTXO的选择。缓解了UTXO数据集的膨胀问题，并控制了交易费用的产生，从而实现了数字钱包服务的性能提升。

Pdf link:

https://ieeexplore.ieee.org/document/10491751

11

Title: 

An SGX and Blockchain-Based System for Federated Learning in Classifying COVID-19 Images

基于 SGX 和区块链的 COVID-19 图像分类联合学习系统

Authors: 

Key words:

Medical Imaging, SGX, Blockchain, Federated Learning, Affiliate Chain

医学影像、SGX、区块链、联邦学习、联盟链

Abstract: 

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes.

联邦学习是一种新兴的分布式学习框架，近年来备受关注。然而，将联邦学习应用于医疗数据时面临两个挑战。第一个挑战源于模型生成节点无法确保计算公平性。第二个挑战涉及通过梯度差异可能泄露私人信息。本文介绍了一种联邦学习系统，该系统利用软件保护扩展（SGX）和区块链技术来增强用于分析胸部影像数据的联邦学习的公平性和安全性。胸部X光数据的影像因子建模可以有效地调节半可信节点的数据质量。区块链智能合约用于数据质量感知证明，质量感知块在不同节点之间经历两阶段重写过程。公证成员模型实现了影像信息学数据的公证集群共识。多通道层模型量化了医疗联盟内参与节点之间计算工作量的分配。在聚合周期中成功验证飞地命令后，将生成可跟踪和打包的签名以支持计算公平性。为确保信息安全，上下游节点间采用SGX远程认证，基于Fabric联邦链平台实现基于SGX和区块链的联邦学习系统，通过横断面联邦学习训练ResNet模型，对新冠病毒肺部医学影像数据进行有效分类。随后进行了多次实验控制和安全分析，发现跨通道时间损失约为21.31%，而SGX的时间开销在节点数允许的5.2%以内。

Pdf link:

https://ieeexplore.ieee.org/document/10491759

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