论社会传染属性的隐私性
On Privacy of Socially Contagious Attributes
摘要：
在数据汇集中保护用户隐私的一种常用方法是在收集之前对用户的真实数据进行随机扰动，以便在不危及个人所持有的秘密的情况下仍然可以推断出聚合的统计数据。在本文中，我们仔细看看差分隐私保证的有效性，当敏感属性受到社会影响和传染。我们首先表明，在没有任何知识的传染网络，一个对手，试图从扰动的预测的真实值，不能实现ROC曲线下面积（AUC）以上$1-(1-δ)/(1+e^\varepsilon)$，如果数据集被扰动使用$(\varepsilon,δ)$ -差分私有机制。然后，我们证明，有了传染网络和模型的知识，人们可以做得更好。我们证明，我们的方法通过差分隐私所施加的性能限制。我们的实验还表明，对他人具有高影响力的节点比其他节点更有可能泄露自己的秘密。通过对合成网络和真实网络的大量实验，显示了其性能。
英文摘要：
A commonly used method to protect user privacy in data collection is to perform randomized perturbation on user's real data before collection so that aggregated statistics can still be inferred without endangering secrets held by individuals. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when the sensitive attributes are subject to social influence and contagions. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot achieve an area under the ROC curve (AUC) above $1-(1-δ)/(1+e^\varepsilon)$, if the dataset is perturbed using an $(\varepsilon,δ)$-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do significantly better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. The performance is shown through extensive experiments on synthetic and real-world networks.
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