CIF: Medium: Collaborative Research: Information-theoretic Guarantees on Privacy in the Age of Learning

CIF：媒介：协作研究：学习时代隐私的信息理论保证

• 批准号: 1900750
• 负责人: Flavio Calmon
• 金额: $ 38.3万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900750&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Information

Armed with powerful advances in machine learning, the ability of an interested party to gather personal information from an individual's expanding digital footprint is outstripping anyone's capability to keep their information private. While this aggregated data can have tremendous benefit for consumers and data scientists via technologies built on machine learning and artificial intelligence, this benefit must be tempered with meaningful assurances of privacy for the very people who provided the data in the first place. This project adopts a rigorous information-theoretic approach to give meaningful privacy guarantees while still providing statistical utility. By combining theoretical and data-driven research, this project can inform public policy as well as best-practices for industry. The overall goal is to provide any data scientist with a set of tools to guarantee meaningful privacy in practice. To do so, this project explores meaningful measures of privacy leakage in the learning context, characterizes the fundamental tradeoffs between privacy and utility, develops techniques to ensure privacy in realistic settings, and tests these algorithms on publicly available datasets. The project is also committed to broadening participation in computing via two outreach efforts: (i) interactive demonstrations of privacy issues that stem from using social media to middle and high school students via ASU's annual STEM event, Open Door, and (ii) teaching modules on machine learning (ML) and artificial intelligence (AI), and short courses ("data jams") at ASU via the Young Engineers Shape the World (YESW) summer program and at Harvard; these modules, targeted at female, financially disadvantaged, and Latino and Hispanic students, aim to make a meaningful contribution to increasing a diverse STEM workforce by providing students hands-on experience on basic concepts of coding, manipulating datasets, and producing simple visualizations collectively. Outreach efforts will be evaluated using well understood metrics for assessment of student interest, engagement, and knowledge via ASU?s College Research and Evaluation Services Team (CREST).This project aims to derive a foundational, statistical theory of privacy that builds upon and contributes to modern theoretical advances in information theory and machine learning. The statistical nature of inference (both for legitimate and illegitimate ends) requires a statistical approach to measuring and ensuring privacy and utility. A significant novel element derived from this view is the maximal alpha leakage, a new, tunable measure for information leakage which quantifies the ability of an adversary to learn any function of private data via a parametric class of loss functions. This tunable measure is derived from a rich information-theoretic framework based on Renyi divergence, thereby uniting disparate existing measures under a single framework. Moreover, its operational significance and computational flexibility allow for natural application in machine learning. In the context of these measures, this project studies privacy-utility tradeoffs both theoretically and in a data-driven manner in two distinct settings: (i) releasing datasets in a similar form as the original, with privacy and strict utility guarantees for arbitrary statistical analysis, and (ii) releasing privacy-guaranteed data representations for specific learning tasks. Broader dissemination of the work will go beyond conferences to organizing a privacy workshop in the latter half of the project to enable inter-disciplinary interactions and application.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着机器学习的强大进步，感兴趣的一方从个人不断扩大的数字足迹中收集个人信息的能力正在超过任何人保持信息隐私的能力。虽然这些聚合数据可以通过建立在机器学习和人工智能基础上的技术为消费者和数据科学家带来巨大好处，但这种好处必须得到有意义的隐私保证，而这些隐私正是最初提供数据的人。这个项目采用了严格的信息论方法，在提供统计效用的同时，提供有意义的隐私保障。通过将理论和数据驱动的研究相结合，该项目可以为公共政策和行业最佳实践提供信息。总体目标是为任何数据科学家提供一套工具，以确保在实践中有意义的隐私。为此，该项目探索了在学习环境中隐私泄露的有意义的测量方法，表征了隐私和效用之间的基本权衡，开发了在现实环境中确保隐私的技术，并在公开可用的数据集上测试了这些算法。该项目还致力于通过两个外展努力扩大对计算机的参与：(I)通过亚利桑那州立大学一年一度的STEM活动Open Door向初中生展示源于使用社交媒体的隐私问题的互动演示，以及(Ii)亚利桑那州立大学通过青年工程师塑造世界(Yesw)暑期计划和哈佛大学关于机器学习(ML)和人工智能(AI)的教学模块和短期课程(数据堵塞)；这些模块面向女性、经济困难的学生以及拉美裔和西班牙裔学生，旨在通过向学生提供编码、操作数据集和集体生成简单可视化的基本概念的实践经验，为增加多样化的STEM劳动力做出有意义的贡献。外展工作将通过亚利桑那州立大学S学院研究和评估服务团队(CREST)使用广为人知的评估学生兴趣、参与度和知识的指标进行评估。该项目旨在得出一个基本的、统计的隐私理论，该理论建立在信息理论和机器学习的现代理论进步的基础上，并对其做出贡献。推论的统计性质(无论是出于合法目的还是非法目的)需要一种统计方法来衡量和确保隐私和效用。从这一观点中衍生出的一个重要的新元素是最大α泄漏，这是一种新的、可调的信息泄漏度量，它量化了对手通过损失函数的参数类学习私人数据的任何功能的能力。这一可调整的衡量标准源于基于Renyi分歧的丰富信息理论框架，从而将不同的现有衡量标准统一在一个框架下。此外，它的操作意义和计算灵活性允许在机器学习中自然应用。在这些措施的背景下，本项目从理论上和以数据驱动的方式在两个不同的背景下研究隐私-效用权衡：(I)以与原始数据相似的形式发布数据集，并为任意统计分析提供隐私和严格的效用保证；(Ii)为特定的学习任务发布隐私保证的数据表示。这项工作的广泛传播将超越会议，在项目的后半部分组织一个隐私研讨会，以实现跨学科的互动和应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs

• DOI: 10.1109/tit.2019.2935768
• 发表时间: 2019-12-01
• 期刊: IEEE TRANSACTIONS ON INFORMATION THEORY
• 影响因子: 2.5
• 作者: Liao, Jiachun;Kosut, Oliver;Calmon, Flavio du Pin
• 通讯作者: Calmon, Flavio du Pin

Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence

局部差分隐私相当于 $f$-Divergence 的收缩

• DOI: 10.1109/isit45174.2021.9517999
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Asoodeh, Shahab;Aliakbarpour, Maryam;Calmon, Flavio P.
• 通讯作者: Calmon, Flavio P.

Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels

• 发表时间: 2021-02
• 期刊: J. Mach. Learn. Res.
• 作者: Hao Wang;Rui Gao;F. Calmon

The Impact of Split Classifiers on Group Fairness

分割分类器对群体公平性的影响

• DOI: 10.1109/isit45174.2021.9517723
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Wang, Hao;Hsu, Hsiang;Diaz, Mario;Calmon, Flavio P.
• 通讯作者: Calmon, Flavio P.

Privacy-Preserving Image Sharing Via Sparsifying Layers on Convolutional Groups

• DOI: 10.1109/icassp40776.2020.9054046
• 发表时间: 2020-02
• 期刊: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Sohrab Ferdowsi;Behrooz Razeghi;T. Holotyak;F. Calmon;S. Voloshynovskiy