TC: Small: Collaborative Research: Membership Inference in a Differentially Private World and Beyond

TC：小：协作研究：差异私人世界及其他世界中的成员资格推断

• 批准号: 1116644
• 负责人: Xinwen Fu
• 金额: $ 16.61万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116644&HistoricalAwards=false
• 关键词: TC; Small; Collaborative; Research; Membership

The objective of the proposed research is to systematically understand, evaluate and contribute towards the problem of membership inference in aggregate data publishing, a generic, novel, and dangerous privacy threat in a wide variety of real-world applications. The main idea proposed to address the problem of membership inference is an information-theoretic model of privacy disclosure as a noisy communication channel. Based on the channel coding theory and the recent advance in multi-input multi-output (MIMO) communication channels, the proposed research studies novel techniques for membership inference and explores the corresponding privacy-preserving mechanisms. Intellectual Merit: The following salient features distinguish the proposed work from existing studies: (1) the proposed research studies a novel problem of membership inference in aggregate data publishing which stands in sharp contrast to the traditional inference control problem. In particular, the sensitive information in danger of disclosure in the proposed problem definition is the selection attributes of an aggregate query instead of its measure attributes which is the focus on traditional inference control. (2) This novel problem also leads to a set of novel solutions based on information theory. In particular, the propose research studies a model of membership inference attacks as modulation techniques in time and frequency domains for various types of communication channels, e.g., single-input single-output (SISO), multiple-input and single-output (MISO), single-input and multiple-output (SIMO), and multiple-input and multiple-output (MIMO) channels. This proposed channel model enables a uniform evaluation of the effectiveness of both membership inference and privacy-preserving techniques. Broader Impact: The outcome of this research has broader impacts on the nation?s higher education system and high-tech industries. The prospect of sensitive membership information disclosure techniques and privacy-preserving techniques can help the providers of aggregated data publishing, including national health organizations, Internet security service providers, etc., to secure their published data. The broader impact of this project also extends to academia. Parts of this project is carried out by students of George Washington University (GWU), Towson University (TU), and University of Massachusetts, Lowell (UML) as part of advanced class projects or individual research projects.

拟议的研究的目的是系统地了解，评估和促进对问题的成员推断聚合数据发布，一个通用的，新颖的，和危险的隐私威胁在各种各样的现实世界的应用。提出解决成员推断问题的主要思想是将隐私泄露作为噪声通信信道的信息理论模型。基于信道编码理论和多输入多输出（MIMO）通信信道的最新进展，研究了新的隶属度推断技术，并探索了相应的隐私保护机制。智力优势：本研究与现有研究的显著区别在于：（1）研究了聚合数据发布中的隶属度推理问题，与传统的推理控制问题形成鲜明对比。特别是，在建议的问题定义中的敏感信息的危险披露是一个聚合查询的选择属性，而不是其测量属性，这是传统的推理控制的重点。(2)这个新的问题也导致了一套基于信息论的新的解决方案。特别地，所提出的研究研究了作为用于各种类型的通信信道的时域和频域中的调制技术的成员推断攻击的模型，例如，单输入单输出（SISO）、多输入单输出（MISO）、单输入多输出（SIMO）和多输入多输出（MIMO）信道。这个建议的通道模型，使成员推理和隐私保护技术的有效性的统一评估。更广泛的影响：这项研究的结果对国家有更广泛的影响？的高等教育体系和高科技产业。敏感会员信息披露技术和隐私保护技术的前景可以帮助聚合数据发布的提供者，包括国家卫生组织、互联网安全服务提供商等，来保护他们发布的数据该项目的广泛影响也延伸到学术界。该项目的部分内容由乔治华盛顿大学（GWU）、陶森大学（TU）和马萨诸塞州洛厄尔大学（UML）的学生作为高级课程项目或个人研究项目的一部分进行。