论文标题
隐私阴影：随着时间的推移测量节点可预测性和隐私
Privacy Shadow: Measuring Node Predictability and Privacy Over Time
论文作者
论文摘要
网络数据的结构使简单的预测模型能够利用节点之间的局部相关性，以高于属性和链接预测等任务的准确性。虽然这对于构建更好的用户模型很有用，但它引入了隐私问题，即用户离开应用程序后可以从网络结构中添加数据。我们提出了隐私的阴影，以测量用户在网络中的任意时间中保持多长时间的预测性。此外，我们证明可以在三个现实世界数据集中的个别用户预测隐私阴影的长度。
The structure of network data enables simple predictive models to leverage local correlations between nodes to high accuracy on tasks such as attribute and link prediction. While this is useful for building better user models, it introduces the privacy concern that a user's data may be re-inferred from the network structure, after they leave the application. We propose the privacy shadow for measuring how long a user remains predictive from an arbitrary time within the network. Furthermore, we demonstrate that the length of the privacy shadow can be predicted for individual users in three real-world datasets.
