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Privacy setting recommendation for image sharing

  • Jun Yu
  • , Zhenzhong Kuang
  • , Zhou Yu
  • , Dan Lin
  • , Jianping Fan
  • Hangzhou Dianzi University
  • Missouri University of Science and Technology
  • University of North Carolina at Charlotte

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

Abstract

This paper aims to simultaneously consider two inseparable issues for privacy setting recommendation: (1) sensitiveness of visual content of the images being shared; and (2) trustworthiness of users being granted. First, an object-based approach is developed for image content sensitiveness (privacy) representation. Secondly, the users on a social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, a tree classifier is trained hierarchically to recommend appropriate privacy settings for image sharing.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages726-730
Number of pages5
ISBN (Electronic)9781538614174
DOIs
StatePublished - 2017
Externally publishedYes
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: 18 Dec 201721 Dec 2017

Publication series

NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume2017-December

Conference

Conference16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/TerritoryMexico
CityCancun
Period18/12/1721/12/17

Keywords

  • Image Sharing
  • Privacy Recommendation
  • Social Network
  • Tree Classifier

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