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，得益于大量数据和强大的计算能力。数据分析在健康、经济和金融等应用领域为社会带来好处的巨大潜力，依赖于保护用户隐私这一根本而紧迫的挑战。在这个项目中，新的理论范式和方法，以解决用户在网络环境中的隐私脆弱性，在大数据的存在进行了研究。该漏洞源于网络中的本地结构依赖性以及网络外部允许用户去匿名化的外源辅助信息的存在。该项目具有变革性的潜力，可以影响用户隐私至关重要的广泛应用程序。项目？的内在跨学科的性质和现实世界的技术潜力补充调查？持续努力让更多的学生（特别是女性和少数民族）学习STEM学科中应用和定量推理交叉的主题。本文的研究工作主要分为三个方面：（1）针对具有相关信息源的攻击者在随机边标号超图中的反匿名问题，建立了信息论转换。这样的对话使得能够推导出必要的条件，在这些条件下，无论有多少计算能力或存储可用，对手都无法对系统进行去匿名化。(2)研究实际可实现的方案：除了校准转换所需的紧密（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转换的必要条件时，攻击者可以学习多少。(3)真实世界的评估：算法的性能和它们的实用性在真实的世界数据集上进行评估。

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.