Rf-protect: Privacy against device-free human tracking

Jayanth Shenoy, Zikun Liu, Bill Tao, Zachary Kabelac, Deepak Vasisht

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The advent of radio sensing that works through walls obstacles challenges the notion of indoor privacy. An eavesdropper can deploy such sensing to snoop on their neighbors and a smart sensor embedded with such sensing capabilities can perform large scale behavioral and health data mining. We present RF-Protect, a new framework that enables privacy by injecting fake humans in the sensed data. RF-Protect consists of a novel hardware reflector design that modifies radio waves to create reflections at arbitrary locations in the environment and a new generative mechanism to create realistic human trajectories. RF-Protect's design doesn't require any high bandwidth hardware or physical motion. We implement RF-Protect using commodity hardware and validate its ability to generate fake human trajectories.

Original languageEnglish (US)
Title of host publicationSIGCOMM 2022 - Proceedings of the ACM SIGCOMM 2022 Conference
PublisherAssociation for Computing Machinery
Pages588-600
Number of pages13
ISBN (Electronic)9781450394208
DOIs
StatePublished - Aug 22 2022
Event2022 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2022 - Amsterdam, Netherlands
Duration: Aug 22 2022Aug 26 2022

Publication series

NameSIGCOMM 2022 - Proceedings of the ACM SIGCOMM 2022 Conference

Conference

Conference2022 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2022
Country/TerritoryNetherlands
CityAmsterdam
Period8/22/228/26/22

Keywords

  • IoT
  • RF
  • embedded systems
  • privacy
  • sensing
  • wireless

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Hardware and Architecture

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