Abstract
Sharing images on social network sites has become a part of daily routine for more and more online users. However, in face of the considerable number of images shared online, it is not a trivial task for a person to manually configure proper privacy settings for each of the images that he/she uploaded. The lack of proper privacy protection during image sharing could raise many potential privacy breaches of people's private lives that they are not aware of. In this work, we propose a privacy setting recommender system to help people effortlessly set up the privacy settings for their online images. The key idea is developed based on our finding that there are certain correlations between a number of generic patterns of image privacy settings and image tags, regardless of the image owners' individual privacy bias and levels of awareness. We propose a multi-pronged mechanism that carefully analyzes tags' semantics and co-presence to derive a set of suitable privacy settings for a newly uploaded image. Our system is also capable of dealing with cold-start problem when there are very few image tags available. We have conducted extensive experimental studies, and the results demonstrate the effectiveness of our approach in terms of the policy recommendation accuracy.
Recommended Citation
A. C. Squicciarini et al., "From Tag to Protect: A Tag-Driven Policy Recommender System for Image Sharing," Proceedings - 2017 15th Annual Conference on Privacy, Security and Trust, PST 2017, pp. 337 - 346, article no. 8476952, Institute of Electrical and Electronics Engineers, Sep 2018.
The definitive version is available at https://doi.org/10.1109/PST.2017.00047
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-153862487-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
28 Sep 2018
- Citations
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- Patent Family Citations: 2
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Comments
National Science Foundation, Grant 1421776