Weili Wang (Southern University of Science and Technology), Honghan Ji (ByteDance Inc.), Peixuan He (ByteDance Inc.), Yao Zhang (ByteDance Inc.), Ye Wu (ByteDance Inc.), Yinqian Zhang (Southern University of Science and Technology)

The advancement of trusted execution environments (TEEs) has enabled the confidential computing paradigm and created new application scenarios for WebAssembly (Wasm). "Wasm+TEE" designs achieve in-enclave multi-tenancy with strong isolation, facilitating concurrent execution of untrusted code instances from multiple users. However, the linear memory model of Wasm lacks efficient cross-module data sharing and fine-grained memory access control, significantly restricting its applications in certain confidential computing scenarios where secure data sharing is essential (e.g., confidential stateful FaaS and data marketplaces). In this paper, we propose WAVEN (WebAssembly Memory Virtualization for ENclaves), a novel WebAssembly memory virtualization scheme, to enable memory sharing among Wasm modules and page-level access control. We implement WAVEN atop WAMR, a popular Wasm runtime for TEEs, and empirically demonstrate its efficiency and effectiveness. To the best of our knowledge, our work represents the first approach that enables cross-module memory sharing with fine-grained memory access control in Wasm.

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TZ-DATASHIELD: Automated Data Protection for Embedded Systems via Data-Flow-Based...

Zelun Kong (University of Texas at Dallas), Minkyung Park (University of Texas at Dallas), Le Guan (University of Georgia), Ning Zhang (Washington University in St. Louis), Chung Hwan Kim (University of Texas at Dallas)

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I Know What You Asked: Prompt Leakage via KV-Cache...

Guanlong Wu (Southern University of Science and Technology), Zheng Zhang (ByteDance Inc.), Yao Zhang (ByteDance Inc.), Weili Wang (Southern University of Science and Technolog), Jianyu Niu (Southern University of Science and Technolog), Ye Wu (ByteDance Inc.), Yinqian Zhang (Southern University of Science and Technology (SUSTech))

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URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

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A Key-Driven Framework for Identity-Preserving Face Anonymization

Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)

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