Yichen Liu (Indiana University Bloomington), Jingwen Yan (Clemson University), Song Liao (Texas Tech University), Long Cheng (Clemson University), Luyi Xing (Indiana University Bloomington)

Privacy compliance has become a significant concern for IoT users as the popularity of diverse IoT devices continues to grow. However, the heterogeneous nature of IoT brings challenges in designing effective privacy-preserving mechanisms. While Matter is a promising unifying connectivity protocol for IoT, it currently offers limited privacy compliance features. In this position paper, we propose the MATTERCOMPLIANCE framework, which achieves privacy compliance by design within the Matter protocol. The design of MATTERCOMPLIANCE follows three principles: providing reliable and proactive privacy disclosure for users, offering interfaces for developers to conveniently integrate privacy mechanisms, and enabling users to manage their privacy settings. By integrating privacy-preserving capabilities in the Matter protocol, MATTERCOMPLIANCE fills the gap in offering a unified solution for privacy compliance in IoT systems.

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Moneta: Ex-Vivo GPU Driver Fuzzing by Recalling In-Vivo Execution...

Joonkyo Jung (Department of Computer Science, Yonsei University), Jisoo Jang (Department of Computer Science, Yonsei University), Yongwan Jo (Department of Computer Science, Yonsei University), Jonas Vinck (DistriNet, KU Leuven), Alexios Voulimeneas (CYS, TU Delft), Stijn Volckaert (DistriNet, KU Leuven), Dokyung Song (Department of Computer Science, Yonsei University)

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DeFiIntel: A Dataset Bridging On-Chain and Off-Chain Data for...

Iori Suzuki (Graduate School of Environment and Information Sciences, Yokohama National University), Yin Minn Pa Pa (Institute of Advanced Sciences, Yokohama National University), Nguyen Thi Van Anh (Institute of Advanced Sciences, Yokohama National University), Katsunari Yoshioka (Graduate School of Environment and Information Sciences, Yokohama National University)

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Duumviri: Detecting Trackers and Mixed Trackers with a Breakage...

He Shuang (University of Toronto), Lianying Zhao (Carleton University and University of Toronto), David Lie (University of Toronto)

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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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