TY - GEN
T1 - Analyzing images' privacy for the modern web
AU - Squicciarini, Anna C.
AU - Caragea, Cornelia
AU - Balakavi, Rahul
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84907414112&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907414112&partnerID=8YFLogxK
U2 - 10.1145/2631775.2631803
DO - 10.1145/2631775.2631803
M3 - Conference contribution
AN - SCOPUS:84907414112
SN - 9781450329545
T3 - HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
SP - 136
EP - 147
BT - HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery
T2 - 25th ACM Conference on Hypertext and Social Media, HT 2014
Y2 - 1 September 2014 through 4 September 2014
ER -