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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TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

Chen Gong (University of Vriginia), Kecen Li (Chinese Academy of Sciences), Jin Yao (University of Virginia), Tianhao Wang (University of Virginia)

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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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Shang Ma (University of Notre Dame), Chaoran Chen (University of Notre Dame), Shao Yang (Case Western Reserve University), Shifu Hou (University of Notre Dame), Toby Jia-Jun Li (University of Notre Dame), Xusheng Xiao (Arizona State University), Tao Xie (Peking University), Yanfang Ye (University of Notre Dame)

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