CAREER: Trustworthy Social Systems Using Network Science

社交媒体系统已经改变了我们的社会交流，包括新闻发现、推荐、社会互动、电子商务以及政治和治理活动。然而，社交媒体系统的日益普及已经把对安全和隐私的担忧带到了最前沿。本项目旨在以网络科学学科为基础，设计可信赖的社会系统。首先，该项目正在开发分析社交媒体数据的技术，以防止个人隐私受到威胁；由于现有的方法无法提供严格的隐私保障，因此需要进行新的研究。其次，该项目正在开发新方法，以减轻社交系统中“虚假账户”的威胁，尽管这些账户的创建者试图逃避检测。部署的和学术的方法仍然容易受到战略对手的攻击，这推动了基于网络科学的新型防御机制的发展。这项研究的发现和新设计将直接影响广大社交网络用户的安全和隐私。私人网络分析的推动力建立在差异隐私的理念之上，确保结果有足够的不确定性，从而隐藏个人关系。该项目引入了依赖差异隐私，它可以防止与个人相关的信息泄露，以及基于熵的相互信息隐私。Sybil的缓解推力基于对抗性机器学习的思想：假设虚假账户的创建者会调整其机制以适应不断变化的检测方法。这项工作利用新的特征，如网络的时间动态，来解决这个问题。最后，该项目旨在将研究与教育倡议相结合，为可信赖的社会系统开发教学方法和内容。

Social media systems have transformed our societal communications, including news discovery, recommendations, societal interactions, E-commerce, as well as political and governance activities. However, the rising popularity of social media systems has brought concerns about security and privacy to the forefront. This project aims to design trustworthy social systems by building on the discipline of network science. First, the project is developing techniques for analysis of social media data that protect against risks to individual privacy; new research is needed since existing approaches are unable to provide rigorous privacy guarantees. Second, the project is developing new approaches to mitigate the threat of "fake accounts" in social systems, in spite of attempts by the creators of those accounts to elude detection. Both deployed and academic approaches remain vulnerable to strategic adversaries, motivating the development of novel defense mechanisms based on network science. The findings and new designs from this research will directly impact the security and privacy of a broad class of social network users.The private network analytics thrust builds on the ideas of differential privacy, ensuring sufficient uncertainty in results to hide individual relationships. The project introduces dependent differential privacy, which protects against disclosure of information associated with an individual, as well as mutual information privacy, an entropy-based measure. The Sybil mitigation thrust is based on the idea of adversarial machine learning: the creators of fake accounts are presumed to adapt their mechanisms to changing detection approaches. This work exploits new features, such as temporal dynamics of the network, to address this problem. Finally, the project aims to integrate the research with an educational initiative for developing pedagogical approaches and content for trustworthy social systems.