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: 1117297
• 负责人: Nan Zhang
• 金额: $ 16.51万
• 依托单位: George Washington University
• 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=1117297&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.

拟议研究的目的是系统地理解、评估和贡献聚合数据发布中的成员推理问题，这是各种现实应用中的通用、新颖且危险的隐私威胁。为解决成员资格推断问题而提出的主要思想是将隐私泄露作为噪声通信渠道的信息论模型。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，该研究研究了成员推理的新技术并探索了相应的隐私保护机制。智力优点：以下显着特征将所提出的工作与现有研究区分开来：（1）所提出的研究研究了聚合数据发布中的成员推理的新问题，这与传统的推理控制问题形成鲜明对比。特别是，所提出的问题定义中面临泄露危险的敏感信息是聚合查询的选择属性，而不是传统推理控制关注的度量属性。 (2) 这个新问题也导致了一系列基于信息论的新解决方案。特别是，本研究研究了成员推理攻击模型，作为各种类型通信信道的时域和频域调制技术，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) 通道。这种提出的通道模型能够对成员资格推断和隐私保护技术的有效性进行统一评估。更广泛的影响：这项研究的成果对国家的高等教育系统和高科技产业具有更广泛的影响。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，保护其发布的数据。该项目的更广泛影响也延伸到了学术界。该项目的部分内容由乔治华盛顿大学 (GWU)、陶森大学 (TU) 和马萨诸塞大学洛厄尔分校 (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。