TWC: Small: Fundamental Limits in Differential Privacy

TWC：小：差异隐私的基本限制

• 批准号: 1527754
• 负责人: Sewoong Oh
• 金额: $ 49.52万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527754&HistoricalAwards=false
• 关键词: TWC; Small; Fundamental; Limits; Differential

Differential Privacy has emerged as a well-grounded approach to balancing personal privacy and societal as well as commercial use of data. The basic idea is to add random noise to analysis results sufficient to obscure the impact of any single individual's data on the analysis, thus protecting individual privacy. While general approaches to providing differential privacy exist, in many cases the bounds are not tight; more noise is added than needed. This project uses information theoretic techniques to explore the fundamental privacy/accuracy tradeoffs in differential privacy. The success of the proposed research will make progress towards a safer and more secure nation where the respect for individuals' privacy is not compromised. The proposed research is strongly integrated with an education plan that aims to develop a new graduate level course on algorithmic foundations of privacy.This project will investigate several topics: (1) characterizing the fundamental tradeoffs between the privacy guarantee and the utility of the released data, by applying information theoretic tools and methods to identify tight bounds on achieving differential privacy; (2) designing data privatization mechanisms for individuals that achieve both computational efficiency and the optimal tradeoffs between utility and privacy; and (3) providing a privacy calculus for macroscopic analyses of complex data processing systems, consisting of various components each with its own privacy guarantees. The privacy calculus aims to provide new representations and computational tools for characterizing how privacy components interact in a large system, analogous to how network calculus allows researchers to characterize complex non-linear communication systems using familiar tools from linear systems.

差异隐私已成为平衡个人隐私和社会以及数据商业使用的一种良好方法。 基本思想是将随机噪声添加到分析结果中，足以掩盖任何个人数据对分析的影响，从而保护个人隐私。 尽管存在提供差异隐私的一般方法，但在许多情况下，界限并不紧密。添加的噪声比需要更多。 该项目使用信息理论技术来探索差异隐私中的基本隐私/准确性权衡。 拟议的研究的成功将使一个更安全，更安全的国家取得进步，在这种国家中，对个人隐私的尊重不遭到损害。拟议的研究与一项教育计划完全融合，该计划旨在开发有关隐私算法基础的新研究生级课程。本项目将调查几个主题：（1）表征隐私保证和释放数据之间的基本权衡，通过应用信息理论和方法来确定与实现差异差异的紧密界限； （2）为个人设计数据私有化机制，这些机制既可以达到计算效率和实用性和隐私之间的最佳权衡； （3）为复杂数据处理系统的宏观分析提供隐私计算，由各种组件组成，每个组件都有其自身的隐私保证。隐私计算旨在提供新的表示和计算工具，以表征隐私组件在大型系统中的相互作用方式，类似于网络演算如何允许研究人员使用线性系统中熟悉的工具来表征复杂的非线性通信系统。