A Group-Based personalized model for image privacy classification and labeling

Haoti Zhong, Anna Squicciarini, David Jonathan Miller, Cornelia Caragea

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

4 Citations (Scopus)

Abstract

We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate "early warnings" with respect to a user's privacy awareness level.

Original languageEnglish (US)
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3952-3958
Number of pages7
ISBN (Electronic)9780999241103
StatePublished - Jan 1 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: Aug 19 2017Aug 25 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period8/19/178/25/17

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Zhong, H., Squicciarini, A., Miller, D. J., & Caragea, C. (2017). A Group-Based personalized model for image privacy classification and labeling. In C. Sierra (Ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 3952-3958). (IJCAI International Joint Conference on Artificial Intelligence). International Joint Conferences on Artificial Intelligence.
Zhong, Haoti ; Squicciarini, Anna ; Miller, David Jonathan ; Caragea, Cornelia. / A Group-Based personalized model for image privacy classification and labeling. 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. editor / Carles Sierra. International Joint Conferences on Artificial Intelligence, 2017. pp. 3952-3958 (IJCAI International Joint Conference on Artificial Intelligence).
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abstract = "We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate {"}early warnings{"} with respect to a user's privacy awareness level.",
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Zhong, H, Squicciarini, A, Miller, DJ & Caragea, C 2017, A Group-Based personalized model for image privacy classification and labeling. in C Sierra (ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, pp. 3952-3958, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 8/19/17.

A Group-Based personalized model for image privacy classification and labeling. / Zhong, Haoti; Squicciarini, Anna; Miller, David Jonathan; Caragea, Cornelia.

26th International Joint Conference on Artificial Intelligence, IJCAI 2017. ed. / Carles Sierra. International Joint Conferences on Artificial Intelligence, 2017. p. 3952-3958 (IJCAI International Joint Conference on Artificial Intelligence).

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

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AB - We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate "early warnings" with respect to a user's privacy awareness level.

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Zhong H, Squicciarini A, Miller DJ, Caragea C. A Group-Based personalized model for image privacy classification and labeling. In Sierra C, editor, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence. 2017. p. 3952-3958. (IJCAI International Joint Conference on Artificial Intelligence).