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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BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS

Yinggang Guo (State Key Laboratory for Novel Software Technology, Nanjing University; University of Minnesota), Zicheng Wang (State Key Laboratory for Novel Software Technology, Nanjing University), Weiheng Bai (University of Minnesota), Qingkai Zeng (State Key Laboratory for Novel Software Technology, Nanjing University), Kangjie Lu (University of Minnesota)

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Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei (Northeastern University)

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BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

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