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Privacy policy inference of multiple user-uploaded images on social context websites (Automated generation of privacy policy)

机译:社交上下文网站上多个用户上传的图像的隐私策略推断(自动生成隐私策略)

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Social networking websites are the most active websites on the Internet and millions of people use them every day to engage and connect with other people. Twitter, Facebook, LinkedIn and Google Plus seems to be the most popular Social networking websites on the Internet. In this manner, recommendation policy is required which supply client with an adaptable help for organizing security settings in much easier way. Images are shared extensively now days on social sharing sites. Sharing takes place between friends and acquaintances on a daily basis. In this thesis, we are implementing an Adaptive Privacy Policy Prediction (A3P) system which will provide users a disturbance free privacy settings experience by automatically generating personalized policies.
机译:社交网站是Internet上最活跃的网站,每天都有数以百万计的人使用它们与他人互动和联系。 Twitter,Facebook,LinkedIn和Google Plus似乎是Internet上最受欢迎的社交网站。以这种方式,需要推荐策略,该推荐策略向客户端提供了以更容易的方式来组织安全设置的适应性帮助。如今,图像已在社交共享网站上广泛共享。朋友和熟人之间每天共享。在本文中,我们正在实现一种自适应隐私策略预测(A3P)系统,该系统将通过自动生成个性化策略为用户提供无干扰的隐私设置体验。

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