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首页> 外文会议>Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >Mining social interactions in privacy-preserving temporal networks
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Mining social interactions in privacy-preserving temporal networks

机译：在保留隐私的时间网络中挖掘社交互动

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摘要

The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time fine-grained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts etc., using systematic methologies from complex networks and graph spectra analysis. However, a misuse of these methods may result in privacy-intrusive and discriminatory actions that may threaten citizens' autonomy and put their life under surveillance. This paper studies highly sparse temporal networks that model social interactions such as the physical proximity of participants in conferences. When citizens can self-determine the anonymized proximity data they wish to share via privacy-preserving platforms, temporal networks may turn out to be highly sparse and have low quality. This paper shows that even in this challenging scenario of privacy-by-design, significant information can be mined from temporal networks such as the correlation of events happening during a conference or stable groups interacting over time. The findings of this paper contribute to the introduction of privacy-preserving data analytics in temporal networks and their applications.
机译：如今，借助物联网技术以及无处不在且无处不在的计算技术，可以实时地细粒度地收集社交网络数据，因此，如今实证研究时态网络的机会非常巨大。这使数据分析和数据科学家可以使用复杂网络中的系统方法和图形频谱分析来推理复杂的时间现象，例如疾病传播，居民能源消耗，政治冲突等。但是，滥用这些方法可能会导致侵犯隐私的行为和歧视性行为，从而威胁到公民的自主权并使其生活受到监视。本文研究了稀疏的时间网络，该网络对社交互动（例如会议参与者的身体接近程度）进行建模。当公民可以自己确定希望通过隐私保护平台共享的匿名邻近数据时，临时网络可能会变得稀疏且质量低下。本文表明，即使在这种设计私密性的挑战性场景中，也可以从时间网络中获取大量信息，例如会议期间发生的事件的相关性或随时间推移进行交互的稳定组。本文的发现有助于在时态网络及其应用中引入隐私保护数据分析。

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