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Monday, 26 February 2018

Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites

With the increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of 

Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites

social context, image content, and metadata as possible indicators of users’ privacy preferences.We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the generated policies will follow the evolution of users’ privacy attitude. We provide the results of our extensive evaluation over 5,000 policies, which demonstrate the effectiveness of our system, with prediction accuracies over 90 percent. Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites
We have proposed an Adaptive Privacy Policy Prediction (A3P) system that helps users automate the privacy policy  settings for their uploaded images. The A3P system provides a comprehensive framework to infer privacy preferences  based on the information available for a given user. We also effectively tackled the issue of cold-start, leveraging social context information. Our  experimental study proves that our A3P is a practical tool that offers significant improvements over current approaches to privacy.
Squicciarini’s work was partially funded by the US National Science Foundation (NSF-CNS-0831360) and a Google Research Award. Lin’s work was funded by the US National Science Foundation (NSF-CNS-1250327) .

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