CIF: Small: Collaborative Research: Analytics on Edge-labeled Hypergraphs: Limits to De-anonymization

数据分析是一个快速增长的领域，这得益于海量数据的可用性和强大的计算能力。数据分析在健康、经济和金融等应用领域为社会提供好处的巨大潜力取决于保护用户隐私的根本和紧迫挑战。在这个项目中，研究了大数据环境下解决用户隐私脆弱性的新的理论范式和方法。该漏洞是由于网络中固有的结构依赖性以及网络外部存在允许用户去匿名化的外部辅助信息造成的。该项目具有变革性的潜力，将对用户隐私至关重要的广泛应用产生影响。项目？S与生俱来的跨学科性质和现实世界的技术潜力是对调查人员的补充？正在努力让更多的学生(特别是妇女和少数群体)学习STEM学科中应用和定量推理相结合的主题。本研究分为三个部分：(1)针对具有相关信息源的敌手的随机边标记超图去匿名化问题的信息论逆的发展。这样的转换可以推导出必要的条件，在这些条件下，无论有多少计算能力或存储空间可用，攻击者都不能使系统去匿名化。(2)研究实用的可实现方案：除了校准逆向所需的紧凑(但不一定有效)可实现的方案外，还探索了实用的去匿名化算法的设计，以量化当发布的数据集不满足逆向必要条件时攻击者可以学习的量。(3)真实世界评估：在真实世界数据集上对算法的性能和实际适用性进行评估。

Data analytics is a rapidly growing field, aided by the availability of huge amounts of data and significant computing power. The immense potential of data analytics to provide benefits to the society in application areas such as health, economics, and finance, is reliant on the fundamental and urgent challenge of protecting privacy of users. In this project, new theoretical paradigms and approaches to address privacy vulnerability of users in network environments in presence of big data are studied. The vulnerability results from the indigenous structural dependencies in the network as well as the presence of exogenous auxiliary information outside of the network that permits deanonymization of the users. This project has transformative potential to impact a broad class of applications where user privacy is critical. The project?s inherently inter-disciplinary nature and real-world technological potential complements the investigators? on-going efforts to engage more students (especially women and minorities) to study topics at the intersection of application and quantitative reasoning in the STEM disciplines. The research is divided into three thrusts: (1) Development of information-theoretic converses for deanonymization problem in random edge-labeled hyper-graphs for adversaries with access to correlated information sources. Such converses enable deriving necessary conditions under which the adversary cannot deanonymize the system, no matter how much computational power or storage is available. (2) Research practical achievable schemes: Besides tight (but not necessarily efficient) achievable schemes required for calibrating the converses, the design of practical deanonymization algorithms to quantify how much attackers can learn when the released datasets do not meet the necessary conditions of the converse, are explored. (3) Real-world evaluations: The performance of the algorithms and their practical applicability are evaluated on real world datasets.