Haotian Chi (Temple University), Qiang Zeng (University of South Carolina), Xiaojiang Du (Temple University), Lannan Luo (University of South Carolina)

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

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Reining in the Web's Inconsistencies with Site Policy

Stefano Calzavara (Università Ca' Foscari Venezia), Tobias Urban (Institute for Internet Security and Ruhr University Bochum), Dennis Tatang (Ruhr University Bochum), Marius Steffens (CISPA Helmholtz Center for Information Security), Ben Stock (CISPA Helmholtz Center for Information Security)

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Detecting DolphinAttacks Based on Microphone Array

Guoming Zhang, Xiaoyu Ji (Zhejiang University)

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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