III: Small: Network Sampling and Construction Methods for Inference and Anonymization

III：小：推理和匿名化的网络采样和构建方法

• 批准号: 1526736
• 负责人: Athina Markopoulou
• 金额: $ 49.98万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526736&HistoricalAwards=false
• 关键词: III; Small; Network; Sampling; Construction

This project will study big network data, including but not limited to those generated by communication using mobile devices and online social media. The increasing availability of such datasets poses both opportunities and challenges. On one hand, their relatively easy collection and analysis facilitate the understanding of online human activity and the design of better services. This, however, requires that targeted answers can be efficiently provided based on limited (sampled or aggregated) data. On the other hand, the combination of big data and powerful inference techniques poses privacy concerns and an increasing need for effective anonymization techniques. This project will address both aspects and will advance the state of the art in network modeling and analysis. In the first part of the project, adaptive link-trace sampling will be designed for ERGM inference of network structure and/or attributes and for node-level analysis. Time series analysis will be applied to aggregate spatio-temporal network activity data to infer and predict patterns. In the second part of the project, novel models and algorithms will be designed in order to generate synthetic networks that resemble real ones with respect to target characteristics of interest. The methods will be applied to simulation and anonymization of network data and can improve the understanding of the tradeoff between utility and privacy in this domain. For further information see the project web site at: http://networkdata.calit2.uci.edu

该项目将研究大网络数据，包括但不限于使用移动的设备和在线社交媒体进行通信所产生的数据。 这些数据集的日益普及既带来了机遇，也带来了挑战。一方面，它们相对容易的收集和分析有助于理解在线人类活动和设计更好的服务。然而，这就要求能够根据有限的（抽样或汇总的）数据有效地提供有针对性的答案。另一方面，大数据和强大的推理技术的结合带来了隐私问题，并越来越需要有效的匿名技术。该项目将解决这两个方面，并将推进网络建模和分析的最新技术。在项目的第一部分，自适应链路跟踪采样将被设计用于网络结构和/或属性的ERGM推断以及节点级分析。时间序列分析将用于汇总时空网络活动数据，以推断和预测模式。在该项目的第二部分，将设计新的模型和算法，以生成与感兴趣的目标特征类似的真实的合成网络。该方法将被应用于网络数据的模拟和匿名化，可以提高在这个领域的效用和隐私之间的权衡的理解。欲了解更多信息，请访问项目网站：http://networkdata.calit2.uci.edu

项目成果

Construction of Directed 2K Graphs

• DOI: 10.1145/3097983.3098119
• 发表时间: 2017-03
• 期刊: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
• 作者: Bálint Tillman;A. Markopoulou;C. Butts;Minas Gjoka