PFIREWALL: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

Haotian Chi, Qiang Zeng, Xiaojiang Du, Lannan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

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 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.

Original languageEnglish
Title of host publication28th Annual Network and Distributed System Security Symposium, NDSS 2021
ISBN (Electronic)1891562665, 9781891562662
DOIs
StatePublished - 2021
Event28th Annual Network and Distributed System Security Symposium, NDSS 2021 - Virtual, Online
Duration: 21 Feb 202125 Feb 2021

Publication series

Name28th Annual Network and Distributed System Security Symposium, NDSS 2021

Conference

Conference28th Annual Network and Distributed System Security Symposium, NDSS 2021
CityVirtual, Online
Period21/02/2125/02/21

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