A scanner darkly: Protecting user privacy from perceptual applications

Suman Jana, Arvind Narayanan, Vitaly Shmatikov

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

70 Scopus citations

Abstract

Perceptual, "context-aware" applications that observe their environment and interact with users via cameras and other sensors are becoming ubiquitous on personal computers, mobile phones, gaming platforms, household robots, and augmented-reality devices. This raises new privacy risks. We describe the design and implementation of DARKLY, a practical privacy protection system for the increasingly common scenario where an untrusted, third-party perceptual application is running on a trusted device. DARKLY is integrated with OpenCV, a popular computer vision library used by such applications to access visual inputs. It deploys multiple privacy protection mechanisms, including access control, algorithmic privacy transforms, and user audit. We evaluate DARKLY on 20 perceptual applications that perform diverse tasks such as image recognition, object tracking, security surveillance, and face detection. These applications run on DARKLY unmodified or with very few modifications and minimal performance overheads vs. native OpenCV. In most cases, privacy enforcement does not reduce the applications' functionality or accuracy. For the rest, we quantify the tradeoff between privacy and utility and demonstrate that utility remains acceptable even with strong privacy protection.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE Symposium on Security and Privacy, SP 2013
Pages349-363
Number of pages15
DOIs
StatePublished - Aug 13 2013
Event34th IEEE Symposium on Security and Privacy, SP 2013 - San Francisco, CA, United States
Duration: May 19 2013May 22 2013

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011

Other

Other34th IEEE Symposium on Security and Privacy, SP 2013
CountryUnited States
CitySan Francisco, CA
Period5/19/135/22/13

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Software
  • Computer Networks and Communications

Keywords

  • Computer vision
  • Privacy

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  • Cite this

    Jana, S., Narayanan, A., & Shmatikov, V. (2013). A scanner darkly: Protecting user privacy from perceptual applications. In Proceedings - 2013 IEEE Symposium on Security and Privacy, SP 2013 (pp. 349-363). [6547120] (Proceedings - IEEE Symposium on Security and Privacy). https://doi.org/10.1109/SP.2013.31