A privacy recommendation wizard for users of social networking sites
A privacy recommendation wizard for users of social networking sites
复制标题
DOI：
10.1145/1866307.1866378
复制
发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
Lujun Fang;Heedo Kim;K. LeFevre;Aaron Tami
中科院分区：
文献类型：
--
作者：
Lujun Fang;Heedo Kim;K. LeFevre;Aaron Tami
隐私对社交网站用户来说是个大问题。虽然像Facebook这样的网站允许个人用户进行个性化的细粒度隐私设置，但事实证明，这对普通用户来说相当困难。这个演示演示了一个机器学习隐私向导，或推荐工具，是我们在密歇根大学建立的。该向导基于以下观察：实际用户根据隐式结构构想他们的隐私偏好（哪些朋友应该看到哪些数据项）。因此，在向用户提出有限数量的精心选择的问题后，通常可以建立一个机器学习模型，准确预测用户的隐私偏好。反过来，这个模型可以用来为用户推荐详细的隐私设置。我们的演示向导作为第三方Facebook应用程序运行。与会者可以通过在自己的Facebook账户上安装向导来“试驾”。
Privacy is a huge problem for users of social networking sites. While sites like Facebook allow individual users to personalize fine-grained privacy settings, this has proven quite difficult for average users. This demonstration illustrates a machine learning privacy wizard, or recommendation tool, that we have built at the University of Michigan. The wizard is based on the underlying observation that real users conceive their privacy preferences (which friends should see which data items) based on an implicit structure. Thus, after asking the user a limited number of carefully-chosen questions, it is usually possible to build a machine learning model that accurately predicts the user's privacy preferences. This model, in turn, can be used to recommend detailed privacy settings for the user. Our demonstration wizard runs as a third-party Facebook application. Conference attendees will be able to "test-drive" the wizard by installing it on their own Facebook accounts.