在图中注入不确定性实现身份混淆
Injecting Uncertainty in Graphs for Identity Obfuscation
摘要：
如今，社交网络应用程序收集的数据为构建新颖的服务创造了迷人的机会，并扩大了我们对社会结构及其动态的理解。不幸的是，由于隐私问题，发布社交网络图表被认为是一种不明智的做法。为了缓解这个问题，已经提出了几种匿名化方法，旨在降低发布数据的隐私泄露风险，同时仍然允许分析它们并得出相关结论。在本文中，我们介绍了一种新的匿名化方法，该方法基于在社交图中注入不确定性并发布由此产生的不确定图。虽然现有的方法通过完全添加或删除边缘来混淆图数据，但我们建议使用更细粒度的扰动来部分添加或删除边缘：这样，我们可以在数据中进行更小的更改，从而保持更高的实用性。我们在真实网络上的实验证实，在相同的身份混淆水平下，我们的方法比现有的发布标准图的随机方法提供了更高的有用性。
英文摘要：
Data collected nowadays by social-networking applications create fascinating opportunities for building novel services, as well as expanding our understanding about social structures and their dynamics. Unfortunately, publishing social-network graphs is considered an ill-advised practice due to privacy concerns. To alleviate this problem, several anonymization methods have been proposed, aiming at reducing the risk of a privacy breach on the published data, while still allowing to analyze them and draw relevant conclusions. In this paper we introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs. While existing approaches obfuscate graph data by adding or removing edges entirely, we propose using a finer-grained perturbation that adds or removes edges partially: this way we can achieve the same desired level of obfuscation with smaller changes in the data, thus maintaining higher utility. Our experiments on real-world networks confirm that at the same level of identity obfuscation our method provides higher usefulness than existing randomized methods that publish standard graphs.
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