Annika Wilde (Ruhr University Bochum), Tim Niklas Gruel (Ruhr University Bochum), Claudio Soriente (NEC Laboratories Europe), Ghassan Karame (Ruhr University Bochum)

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good “marriage”: TEEs bring confidential computing to the blockchain while the consensus layer could help defend TEEs from forking attacks.

In this paper, we systemize how current blockchain solutions integrate TEEs and to what extent they are secure against forking attacks. To do so, we thoroughly analyze 29 proposals for TEE-based blockchains, ranging from academic proposals to production-ready platforms. We uncover a lack of consensus in the community on how to combine TEEs and blockchains. In particular, we identify four broad means to interconnect TEEs with consensus, analyze their limitations, and discuss possible remedies. Our analysis also reveals previously undocumented forking attacks on three production-ready TEE-based blockchains: Ten, Phala, and the Secret Network. We leverage our analysis to propose effective countermeasures against those vulnerabilities; we responsibly disclosed our findings to the developers of each affected platform.

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DShield: Defending against Backdoor Attacks on Graph Neural Networks...

Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao Xiang (Chongqing University), Meng Shen (Beijing Institute of Technology, Beijing, China), Xinwang Liu (National University of Defense Technology)

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CCTAG: Configurable and Combinable Tagged Architecture

Zhanpeng Liu (Peking University), Yi Rong (Tsinghua University), Chenyang Li (Peking University), Wende Tan (Tsinghua University), Yuan Li (Zhongguancun Laboratory), Xinhui Han (Peking University), Songtao Yang (Zhongguancun Laboratory), Chao Zhang (Tsinghua University)

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Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

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