TargetFinder: Privacy preserving target search through IoT cameras

Youssef Khazbak, Junpeng Qiu, Tianxiang Tan, Guohong Cao

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

Abstract

With the proliferation of IoT cameras, it is possible to use crowd-sourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle, etc.) on demand. Due to the ubiquity of IoT cameras such as dash mounted cameras and phone camera, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and thus can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homo-morphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target's face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.

Original languageEnglish (US)
Title of host publicationIoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation
EditorsGowri Sankar Ramachandran, Jorge Ortiz
PublisherAssociation for Computing Machinery, Inc
Pages213-224
Number of pages12
ISBN (Electronic)9781450362832
DOIs
StatePublished - Apr 15 2019
Event4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019 - Montreal, Canada
Duration: Apr 15 2019Apr 18 2019

Publication series

NameIoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation

Conference

Conference4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019
CountryCanada
CityMontreal
Period4/15/194/18/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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  • Cite this

    Khazbak, Y., Qiu, J., Tan, T., & Cao, G. (2019). TargetFinder: Privacy preserving target search through IoT cameras. In G. S. Ramachandran, & J. Ortiz (Eds.), IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation (pp. 213-224). (IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation). Association for Computing Machinery, Inc. https://doi.org/10.1145/3302505.3310083