Xiaofei Bai (School of Computer Science, Fudan University), Jian Gao (School of Computer Science, Fudan University), Chenglong Hu (School of Computer Science, Fudan University), Liang Zhang (School of Computer Science, Fudan University)

Blockchain networks, especially cryptocurrencies, rely heavily on proof-of-work (PoW) systems, often as a basis to distribute rewards. These systems require solving specific puzzles, where Application Specific Integrated Circuits (ASICs) can be designed for performance or efficiency. Either way, ASICs surpass CPUs and GPUs by orders of magnitude, and may harm blockchain networks. Recently, Equihash is developed to resist ASIC solving with heavy memory usage. Although commercial ASIC solvers exist for its most popular parameter set, such solvers do not work under better ones, and are considered impossible under optimal parameters. In this paper, we inspect the ASIC resistance of Equihash by constructing a parameter-independent adversary solver design. We evaluate the product, and project at least 10x efficiency advantage for resourceful adversaries. We contribute to the security community in two ways: (1) by revealing the limitation of Equihash and raising awareness about its algorithmic factors, and (2) by demonstrating that security inspection is practical and useful on PoW systems, serving as a start point for future research and development.

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Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

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Zheng Leong Chua (National University of Singapore), Yanhao Wang (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences), Teodora Baluta (National University of Singapore), Prateek Saxena (National University of Singapore), Zhenkai Liang (National University of Singapore), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences)

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Samuel Weiser (Graz University of Technology), Mario Werner (Graz University of Technology), Ferdinand Brasser (Technische Universität Darmstadt), Maja Malenko (Graz University of Technology), Stefan Mangard (Graz University of Technology), Ahmad-Reza Sadeghi (Technische Universität Darmstadt)

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Jinfeng Li (Zhejiang University), Shouling Ji (Zhejiang University), Tianyu Du (Zhejiang University), Bo Li (University of California, Berkeley), Ting Wang (Lehigh University)

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