CIF: Small: Statistical Data Privacy: Fundamental Limits and Efficient Algorithms

CIF：小：统计数据隐私：基本限制和高效算法

• 批准号: 1422278
• 负责人: Pramod Viswanath
• 金额: $ 50万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422278&HistoricalAwards=false
• 关键词: CIF; Small; Statistical; Data; Privacy

Privacy is a fundamental individual right. In the era of big data, large amounts of data about individuals are collected both voluntarily (e.g., frequent flier/shopper incentives) and involuntarily (e.g. US Census or medical records). With the ready ability to search for information and correlate it across distinct sources (using data analytics and/or recommender systems), privacy violation takes on an ominous note in this information age. Anonymization of user information is a classical technique, but is susceptible to correlation attacks: by correlating the anonymized database with another (perhaps publicly available) deanonymized database, a user's privacy could still be divulged. A way out of the limitations of anonymization is to release a randomized database; this offers plausible deniability of any user identity breached via the data release. A systematic way of providing guarantees for the deniability of user presence/absence is the technical field of differential privacy, providing strong privacy guarantees against adversaries with arbitrary side information. It is of fundamental interest to characterize privacy mechanisms that randomize "just enough" to keep the released database as true to the intended one as possible, providing maximal utility. Based on recent work connecting the areas of information theory and statistical data privacy (via a hypothesis testing context) and demonstrating novel privacy mechanisms that exponentially improve (in terms of variance of noise added, say) upon the state of the art for medium and low privacy regimes, the objective of the project is threefold: (a) characterize the fundamental limits to tradeoffs between privacy and utility in a variety of canonical setting; (b) discover (near) optimal mechanisms that can be efficiently implemented in practice; and (c) seek natural notions of statistical data privacy (beyond differential privacy) using the operational context of hypothesis testing. Privacy is a central, and multifaceted, social and technological issue of today's information age. This project is focused on the technical aspect of this multifaceted area, and seeks to discover fundamental limits to privacy-utility tradeoffs in the context of currently well established notions of privacy (differential privacy). The expected results expected are fundamental and immediately applicable to a variety of practical settings. Specifically, two concrete practical settings involving genomic data release and smart meter data release will be studied in detail. Due to privacy concerns, genomic and smart meter data is simply unavailable at large -- depriving widespread data analytics and practical implications of such analysis. This project will build and release a software suite of sanitization tools, involving the privacy mechanisms discovered as part of this project.

隐私是一项基本的个人权利。在大数据时代，大量有关个人的数据是自愿收集的（例如，常旅客/购物者奖励）和非自愿收集的（例如美国人口普查或医疗记录）。 由于具备搜索信息并将其跨不同来源关联起来（使用数据分析和/或推荐系统）的能力，侵犯隐私在这个信息时代呈现出不祥的迹象。用户信息的匿名化是一种经典技术，但容易受到关联攻击：通过将匿名数据库与另一个（可能是公开可用的）去匿名数据库相关联，用户的隐私仍然可能被泄露。摆脱匿名化局限性的一个方法是发布随机数据库；这为任何通过数据发布而泄露的用户身份提供了合理的否认。为用户存在/缺席的可否认性提供保证的系统方法是差分隐私技术领域，针对具有任意辅助信息的对手提供强大的隐私保证。表征隐私机制具有根本意义，该机制可以“刚好足够”随机化，以保持发布的数据库尽可能真实地符合预期数据库，从而提供最大的效用。 基于最近将信息论和统计数据隐私领域联系起来的工作（通过假设检验背景），并展示了新颖的隐私机制，该机制在中低水平的现有技术基础上呈指数级改进（例如，就添加的噪声方差而言）隐私制度，该项目的目标有三个：（a）描述各种规范环境中隐私和实用性之间权衡的基本限制； (b) 发现可以在实践中有效实施的（接近）最佳机制； (c) 使用假设检验的操作环境寻求统计数据隐私的自然概念（超越差异隐私）。隐私是当今信息时代的一个核心、多方面的社会和技术问题。该项目专注于这个多方面领域的技术方面，并寻求在当前完善的隐私概念（差异隐私）的背景下发现隐私与效用权衡的基本限制。预期的预期结果是根本性的，并且可以立即适用于各种实际情况。具体来说，将详细研究涉及基因组数据发布和智能电表数据发布的两个具体实践设置。由于隐私问题，基因组和智能电表数据根本无法获得，从而剥夺了广泛的数据分析和此类分析的实际意义。该项目将构建并发布一套清理工具软件，其中涉及作为该项目一部分发现的隐私机制。