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

CIF：小型：协作研究：边缘标记超图分析：去匿名化的限制

• 批准号: 1944993
• 负责人: Siva Theja Maguluri
• 金额: $ 22.75万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944993&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Analytics

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.

数据分析是一个快速发展的领域，得益于大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利益的巨大潜力依赖于保护用户隐私这一基本而紧迫的挑战。在本项目中，研究了解决大数据存在的网络环境中用户隐私脆弱性的新理论范式和方法。漏洞源于网络中固有的结构依赖，以及网络外部允许用户去匿名化的外生辅助信息的存在。这个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。这个项目吗?S固有的跨学科性质和现实世界的技术潜力补充了研究人员？继续努力让更多的学生（尤其是女性和少数族裔）学习STEM学科中应用和定量推理交叉的主题。本研究分为三个重点：(1)针对具有相关信息源的对手的随机边缘标记超图的去匿名化问题，开发了信息论逆算法。这样的交换可以产生必要的条件，在这种条件下，无论有多少计算能力或存储空间，对手都无法使系统去匿名化。(2)研究实用的可实现方案：除了校准反转所需的严格（但不一定是高效）可实现方案外，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反转的必要条件时攻击者可以学习多少。(3)真实世界的评估：在真实世界的数据集上评估算法的性能及其实际适用性。