Xin Wang (Tsinghua University and State Key Laboratory of Cryptography and Digital Economy Security), Haochen Wang (Tsinghua University), Haibin Zhang (Yangtze Delta Region Institute of Tsinghua University, Zhejiang), Sisi Duan (Tsinghua University, Zhongguancun Laboratory, Shandong Institute of Blockchains and State Key Laboratory of Cryptography and Digital Economy Security)

Byzantine fault-tolerant (BFT) protocols are known to suffer from the scalability issue. Indeed, their performance degrades drastically as the number of replicas n grows. While a long line of work has attempted to achieve the scalability goal, these works can only scale to roughly a hundred replicas, particularly on low-end machines.

In this paper, we develop BFT protocols from the so-called committee sampling approach that selects a small committee for consensus and conveys the results to all replicas. Such an approach, however, has been focused on the Byzantine agreement (BA) problem (considering replicas only) instead of the BFT problem (in the client-replica model); also, the approach is mainly of theoretical interest only, as concretely, it works for impractically large n.

We build an extremely efficient, scalable, and adaptively secure BFT protocol called Pando in partially synchronous environments based on the committee sampling approach. Our evaluation on Amazon EC2 shows that in contrast to existing protocols, Pando can easily scale to a thousand replicas in the WAN environment, achieving a throughput of 62.57 ktx/sec.

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MES: Thwarting Fuzzing by Suppressing Memory Errors (Registered Report)

Fannv He (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China, and School of Cyberspace Security, Hainan University, China), Yuan Liu (School of Cyber Engineering, Xidian University, China), Jice Wang (School of Cyberspace Security, Hainan University, China), Baiquan Wang (School of Cyberspace Security, Hainan University, China), Zezhong Ren (National Computer Network…

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FirmAgent: Leveraging Fuzzing to Assist LLM Agents with IoT...

Jiangan Ji (Information Engineering University,Tsinghua University), Chao Zhang (Tsinghua University), Shuitao Gan (Labortory for Advanced Computing and Intelligence Engineering), Lin Jian (Information Engineering University), Hangtian Liu (Information Engineering University), Tieming Liu (Information Engineering University), Lei Zheng (Tsinghua university), Zhipeng Jia (Information Engineering University)

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NetCap: Data-Plane Capability-Based Defense Against Token Theft in Network...

Osama Bajaber (Virginia Tech), Bo Ji (Virginia Tech), Peng Gao (Virginia Tech)

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