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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RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command...

Jiangyi Deng (Zhejiang University), Xinfeng Li (Zhejiang University), Yanjiao Chen (Zhejiang University), Yijie Bai (Zhejiang University), Haiqin Weng (Ant Group), Yan Liu (Ant Group), Tao Wei (Ant Group), Wenyuan Xu (Zhejiang University)

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Revisiting EM-based Estimation for Locally Differentially Private Protocols

Yutong Ye (Institute of software, Chinese Academy of Sciences & Zhongguancun Laboratory, Beijing, PR.China.), Tianhao Wang (University of Virginia), Min Zhang (Institute of Software, Chinese Academy of Sciences), Dengguo Feng (Institute of Software, Chinese Academy of Sciences)

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Work-in-Progress: Uncovering Dark Patterns: A Longitudinal Study of Cookie...

Zihan Qu (Johns Hopkins University), Xinyi Qu (University College London), Xin Shen, Zhen Liang, and Jianjia Yu (Johns Hopkins University)

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