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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Target-Centric Firmware Rehosting with Penguin

Andrew Fasano, Zachary Estrada, Luke Craig, Ben Levy, Jordan McLeod, Jacques Becker, Elysia Witham, Cole DiLorenzo, Caden Kline, Ali Bobi (MIT Lincoln Laboratory), Dinko Dermendzhiev (Georgia Institute of Technology), Tim Leek (MIT Lincoln Laboratory), William Robertson (Northeastern University)

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LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

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Diffence: Fencing Membership Privacy With Diffusion Models

Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

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