PRIVACY POLICY RECOMMENDATION FOR USER UPLOADED IMAGES ON SOCIAL SITES

Computer Engineering

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 advertised incidents where users unknowingly shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is obvious. 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 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.

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