CAREER: Trustworthy Social Systems Using Network Science

社交媒体系统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流行将人们对安全和隐私的担忧推向了前台。该项目旨在通过建立在网络科学学科的基础上，设计可信赖的社会系统。首先，该项目正在开发社交媒体数据分析技术，以保护个人隐私免受风险；由于现有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，该项目正在开发新的方法，以减轻社会系统中“虚假账户”的威胁，尽管这些账户的创建者试图躲避检测。部署的和学术的方法仍然容易受到战略对手的攻击，这促使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的设计将直接影响广泛的社交网络用户的安全和隐私。私有网络分析的推动力建立在差异隐私的想法上，确保结果有足够的不确定性来隐藏个人关系。该项目引入了独立差异隐私，它防止与个人相关的信息泄露，以及相互信息隐私，这是一种基于熵的衡量标准。Sybil的缓解推力基于对抗性机器学习的想法：假冒账户的创建者被认为会调整他们的机制，以适应不断变化的检测方法。这项工作利用了新的特征，如网络的时间动力学，来解决这个问题。最后，该项目旨在将这项研究与一项教育倡议结合起来，为值得信赖的社会系统制定教学方法和内容。

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.