Towards Automated Content-based Photo Privacy Control in User-Centered Social Networks

Nishant Vishwamitra, Yifang Li, Hongxin Hu, Kelly Caine, Long Cheng, Ziming Zhao, Gail Joon Ahn

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

Abstract

A large number of photos shared online often contain private user information, which can cause serious privacy breaches when viewed by unauthorized users. Thus, there is a need for more efficient privacy control that requires automatic detection of users' private photos. However, the automatic detection of users' private photos is a challenging task, since different users may have different privacy concerns and a generalized one-size-fits-all approach for private photo detection would not be suitable for most users. User-specific detection of private photos should, therefore, be investigated. Furthermore, for effective privacy control, the exact sensitive regions in private photos need to be pinpointed, so that sensitive content can be protected via different privacy control methods. In this paper, we propose a novel system, AutoPri, to enable automatic and user-specific content-based photo privacy control in online social networks. We collect a large dataset of 31, 566 private and public photos from real-world users and present important observations on photo privacy concerns. Our system can automatically detect private photos in a user-specific manner using a detection model based on a multimodal variational autoencoder and pinpoint sensitive regions in private photos with an explainable deep learning-based approach. Our evaluations show that AutoPri can effectively determine user-specific private photos with high accuracy (94.32%) and pinpoint exact sensitive regions in them to enable effective privacy control in user-centered online social networks.

Original languageEnglish (US)
Title of host publicationCODASPY 2022 - Proceedings of the 12th ACM Conference on Data and Application Security and Privacy
PublisherAssociation for Computing Machinery, Inc
Pages65-76
Number of pages12
ISBN (Electronic)9781450392204
DOIs
StatePublished - Apr 14 2022
Externally publishedYes
Event12th ACM Conference on Data and Application Security and Privacy, CODASPY 2022 - Virtual, Online, United States
Duration: Apr 24 2022Apr 27 2022

Publication series

NameCODASPY 2022 - Proceedings of the 12th ACM Conference on Data and Application Security and Privacy

Conference

Conference12th ACM Conference on Data and Application Security and Privacy, CODASPY 2022
Country/TerritoryUnited States
CityVirtual, Online
Period4/24/224/27/22

Keywords

  • deep learning
  • privacy control
  • social media

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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