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User-shared images are shared on social media about a user’s life and interests that are widely accessible to others due to their sharing nature. Unlike for online profiles and social graphs, most users are unaware of the privacy risks relating to shared images, as they do not directly disclose characteristics such as gender and origin. Recently, however, user-shared images have been proven to be an accessible alternative to social graphs for online friendship recommendation and gender identification. This article evaluates 1.6M user-shared images from an image-oriented social network, Fotolog, and concludes how they can create privacy risks by proposing a system for de-anonymization, as well as inferring information on online profiles with the user-shared images. It is concluded that given user-shared images, using social graphs is 2 and 2.5 times more effective in de-anonymization than using origins or genders. With two showcases, it is also proven that using user-shared images is effective in online friendship recommendation, gender identification, and origin inference. To the best of our knowledge, this is the first article to evaluate the privacy issue qualitatively with big multimedia data from a real social network.
Cheung et al. (Thu,) studied this question.
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