Analyzing images' privacy for the modern web

Anna Squicciarini, Cornelia Caragea, Rahul Balakavi

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

20 Scopus citations

Abstract

Images are now one of the most common form of content shared in online user-contributed sites and social Web 2.0 applications. In this paper, we present an extensive study exploring privacy and sharing needs of users' uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. We focus on a set of visual-content features and on tags. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users' images. We consider both the case of binary privacy settings (i.e. public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve extremely high accuracy, especially when high-quality tags are available.

Original languageEnglish (US)
Title of host publicationHT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery
Pages136-147
Number of pages12
ISBN (Print)9781450329545
DOIs
StatePublished - Jan 1 2014
Event25th ACM Conference on Hypertext and Social Media, HT 2014 - Santiago, Chile
Duration: Sep 1 2014Sep 4 2014

Publication series

NameHT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media

Other

Other25th ACM Conference on Hypertext and Social Media, HT 2014
CountryChile
CitySantiago
Period9/1/149/4/14

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

Cite this

Squicciarini, A., Caragea, C., & Balakavi, R. (2014). Analyzing images' privacy for the modern web. In HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media (pp. 136-147). (HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media). Association for Computing Machinery. https://doi.org/10.1145/2631775.2631803