近日，实验室在区块链底层分片系统的研究取得新进展，论文《BrokerChain: A Cross-Shard Blockchain Protocol for Account/Balance based State Sharding》被计算机网络领域的CCF-A类顶会 INFOCOM 2022 接收。INFOCOM (全称 IEEE International Conference on Computer Communications) 是计算机网络领域的顶级会议。本次会议共投稿1129篇论文，最终接收了225篇，接收率为19.9%。
Our paper titled “Revisiting Double-Spending Attacks on the Bitcoin Blockchain: New Findings” is going to appear in IEEE / ACM International Symposium on Quality of Service 2021 (IWQoS 2021), which is going to be held on June 25-28, 2021.
Although double-spending attacks (DSA) have created a giant loss to Bitcoin, we believe that advanced versions of DSA can be developed to create new threats for the Bitcoin ecosystem. To this end, this paper presents a new type of double-spending attack, named Adaptive DSA.
Through the proposed analytical model and the disclosed insights behind Adaptive DSA, we aim to Alert the PoW-based cryptocurrency ecosystem that the threat of double-spending attacks is still at a high level.
近日，实验室在区块链性能优化领域的研究取得新进展，论文《MVCom: Scheduling Most Valuable Committees for the Large-Scale Sharded Blockchain》被分布式计算顶级学术会议The 41st IEEE International Conference on Distributed Computing Systems (ICDCS 2021) 录用为长文。
会议介绍
ICDCS是分布式计算系统领域享有盛誉和具有重要学术影响力的顶级国际学术会议，本届 ICDCS 会议 Research Track 论文全球投稿共489篇，仅有97篇被录用，录用率为19.8%。
论文介绍
Huawei Huang, Zhenyi Huang, Xiaowen Peng, Zibin Zheng, Song Guo, “MVCom: Scheduling Most Valuable Committees for the Large-Scale Sharded Blockchain”, ICDCS, 2021. [RG-Page & PDF]
I would like to share our latest blockchain survey titled “A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools”. This survey is focusing on the theoretical modelings, analytical models, and evaluation tools of blockchains.
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近日，我们在 arXiv 公开了最新的一篇区块链综述论文，论文题目为 “A Survey of State-of-the-Art on Blockchains: Theories, Modelings, and Tools”. 比起现有的其他区块链的综述论文，这篇综述主要从理论建模、分析模型、实验评估工具的角度对区块链本身的基础运行机制进行了探讨。希望这篇综述论文可以为研究者、工程开发者、以及从事区块链教育的业内人士提供一个具有参考价值手册。
This blog introduces the motivation and background of one of my previous research articles, which has the following publish information:
Huawei Huang, Song Guo, Weifa Liang, Keqiu Li, Baoliu Ye, and Weihua Zhuang, “Near-Optimal Routing Protection for In-Band Software-Defined Heterogeneous Networks”, IEEE Journal on Selected Areas in Communications (JSAC), vol. 16, no. 20, pp. 7421-7432, November 2016. (CCF-A, Computer Networks)
Photo by Thomas Jensen on Unsplash
Perspectives
Writing this article was a great pleasure because the proposed algorithm provides optimal routing protection for control-plane traffic in the in-band fashioned software-defined networks. Importantly, the proposed approach can be extended to the general routing protection in the data plane of Software-define Networks.
What is it about?
In the software-defined heterogeneous networks, we study a weighted cost minimization problem, in which the control-plane traffic load balancing and control-channel setup cost are jointly considered when selecting the protection paths for control channels. Since the multiple resource-constrained routing is proved to be NP-complete, we propose a near-optimal algorithm, using the Markov approximation technique. Particularly, we extend our solution to an online case that can handle dynamic single-link failures. The incurred performance fluctuation is also theoretically analyzed.
Why is it important?
Even though SDN brings quantities of advantages to the software-defined heterogeneous network (HetNet), it comes with many challenges. One particular concern is the resilience of the control traffic, i.e., the communications between data-plane and control-plane. In an in-band fashioned software-defined heterogeneous network, where control-plane traffic shares medium with the data plane traffic, even a single link failure may disconnect a large number of packet-switching devices from their controllers, resulting in much worse damages than those of the out-of-band fashion. For example, in case of failures caused by disaster scenarios such as earthquake and tsunami, the core network links between switches and controllers may be disconnected. That would result in severe performance degradation, including packet loss, loop routing, suboptimal or infeasible routing actions, high network latency, and even service unavailability. The consequence becomes even worse in wide-area software-defined HetNets. Therefore, to deal with routing protection at the control plane for in-band HetNets is a fundamental issue.
=============== Chinese Version ================
为软件定义网络的控制信道提供可靠的路由策略 – 回顾一篇发表在JSAC的代表作
这篇blog介绍我之前的一篇发表在JSAC的技术论文，论文信息如下：
Huawei Huang, Song Guo, Weifa Liang, Keqiu Li, Baoliu Ye, and Weihua Zhuang, “Near-Optimal Routing Protection for In-Band Software-Defined Heterogeneous Networks”, IEEE Journal on Selected Areas in Communications (JSAC), vol. 16, no. 20, pp. 7421-7432, November 2016.(CCF-A类, 计算机网络)
图 1. The proposed PIRATE framework has two critical components: 1) reliability assessment; 2) blockchain (BC) systems for distributed SGD (D-SGD). Gradient aggregations and model parameters are protected by blockchains. Meanwhile, reliability assessment determines the participants of distributed learning tasks. 图 2. While adopting the “Ring Allreduce” mechanism, malicious attackers can perform attacking from both inside and outside. (1) Attackers from the outside can contaminate training models in target nodes. (2) The outside attackers can also attack partial gradient-aggregation. (3) Byzantine computing nodes can send harmful aggregations that damage the convergence of learning tasks.
理论上来讲，恶意攻击者可以攻击分布式机器学习的各个环节。针对妨碍训练收敛的任意攻击行为，我们提出了图1所示的 PIRATE：一个基于区块链的安全分布式学习框架。由于区块链技术验证的灵活性，此框架可不限于保护本文中所述的训练收敛性，而是在其他安全保护方面也具有巨大的潜能。基于本框架，更多的保护机制可以被开发出来，如针对参与分布式学习的设备进行隐私保护，针对 Model Poisoning Attack 的保护，为所有参与者提供激励机制等。
我们采取了去中心化的 Ring AllReduce 结构（图2）。这种架构可以更好地分担网络压力，并且可以在验证计算结果的同时进行梯度计算。同时，为了让节点在 Ring AllReduce 结构下高效、可验证地沟通，我们运用了基于分片的区块链。其中，我们把所有节点分成多个委员会，每个节点只需要验证自己委员会内的梯度计算。这种分布式验证极大地降低了广播所带来的时延。
News: The paper titled “PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks” has been accepted by IEEE Network (IF: 9.59) on Aug. 11th, 2020.
Introduction:
With the production cost of AI chips gradually reduced to an acceptable level, mobile devices are better equipped with computational resources for machine learning. Meanwhile, as the bottleneck of distributed machine learning, network conditions would be substantially improved as we march into the 5G era. To exploit the merits of 5G/6G networks, a large-scale distributed learning framework is in need. Particularly, in the large-scale scenario, security problems become even more critical.
To protect against arbitrary convergence hindrance attacks, we propose PIRATE, a blockchain-based secure distributed learning framework. The framework has great potential utilizing the verification flexibility of blockchain techniques. Such flexibility enables more protection mechanisms to be built on top of the framework, e.g., privacy protection, Model Poisoning Attack protection, incentive mechanism, etc.
As shown in Figure 1, PIRATE has two components: 1) reliability assessment, which decides whether a device could take part in a learning task; 2) a secure SGD framework based on multiple shard chains.
We utilize the decentralized architecture, Ring AllReduce (Figure 2), which can better leverage network resources, and enables devices to verify computation results while computing gradients.
Furthermore, in order to conduct efficient and verifiable communication under the Ring AllReduce setting, we utilize a sharding-based blockchain technique. In particular, we divide nodes into multiple committees, in which nodes are only required to verify gradients within their committee. Such division greatly reduces the latency of broadcasting.
Simulation experiments show that, under the condition of 5G/6G networks, relatively large training models, and large-scale participants, PIRATE outperforms a similar framework, LearningChain, in terms of storage complexity and latency.
Please feel free to download and read from the following URLs:
