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

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其信息私密性的能力。虽然通过基于机器学习和人工智能的技术，这种聚合数据可以为消费者和数据科学家带来巨大的好处，但这种好处必须与对最初提供数据的人的隐私有意义的保证相协调。该项目采用严格的信息理论方法，在提供统计效用的同时提供有意义的隐私保证。通过结合理论和数据驱动的研究，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。要做到这一点，该项目探讨了学习环境中隐私泄露的有意义的措施，描述了隐私和实用性之间的基本权衡，开发了在现实环境中确保隐私的技术，并在公开的数据集上测试了这些算法。该项目还致力于通过两项外联工作扩大对计算的参与：（i）通过ASU的年度STEM活动Open Door，向中学生展示使用社交媒体产生的隐私问题的互动演示，以及（ii）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”）在亚利桑那州立大学通过青年工程师塑造世界（YESW）夏季计划和在哈佛;这些模块，针对女性，经济困难，拉丁美洲和西班牙裔学生，旨在通过为学生提供编码，操作数据集和集体制作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。外展工作将使用良好理解的指标进行评估，通过ASU学生的兴趣，参与和知识评估？该项目的目的是获得一个基础的，隐私的统计理论，建立在信息理论和机器学习的现代理论进步，并作出贡献。推理的统计性质（合法和非法目的）需要一种统计方法来衡量和确保隐私和效用。一个重要的新元素来自这个观点是最大的阿尔法泄漏，一个新的，可调的信息泄漏的措施，量化的能力的对手学习任何功能的私人数据通过一个参数类的损失函数。这个可调的措施是从一个丰富的信息理论框架的基础上Renyi分歧，从而统一在一个单一的框架下不同的现有措施。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，该项目在两种不同的环境中以理论和数据驱动的方式研究隐私-效用权衡：（i）以与原始数据类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，以及（ii）为特定的学习任务发布隐私保证的数据表示。更广泛地传播工作将超越会议，在项目的后半期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，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

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.