CAREER: Practical Privacy and Fairness for Data-Driven Applications

职业：数据驱动应用程序的实用隐私和公平

• 批准号: 1943016
• 负责人: Matthew Fredrikson
• 金额: $ 55万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943016&HistoricalAwards=false
• 关键词: CAREER; Practical; Privacy; Fairness; Data

Data-driven applications play an increasing role in peoples’ lives, underpinning systems and services that collect broad personal information to provide novel functionality and valuable insights. Machine learning techniques are predominantly used to implement these applications, and developers have published an array of libraries that make it easy for any programmer to benefit from this technology. While excitement over these developments has led to numerous positive contributions, it has also been accompanied by concerns around the privacy of individuals’ data, and the potential for these systems to discriminate against some individuals. This project aims to move ahead of these problems by exploring verification techniques for uncovering instances of protected information use that lead to privacy loss and discrimination. Inspired by recent advances that allow attribution of predictions in machine learning models, we build on methods from software model checking and optimization to locate components pivotal to these outcomes, and construct data representations that aid in removing them. In parallel, we are developing a deeper understanding of new types of software "bugs" that result in such harms: bias amplification, which imperils fairness, and exploitable data memorization, which introduces privacy risk. We aim to quantify the extent to which existing techniques can prevent the occurrence of these bugs, and inform the development of new ones that are specifically targeted at them. As this project progresses, we are applying the results towards educating a diverse workforce on data privacy, algorithmic fairness, and rigorous approaches to constructing software that uses machine learning effectively.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.

数据驱动的应用程序在人们的生活中发挥着越来越大的作用，支持收集广泛个人信息以提供新功能和有价值见解的系统和服务。机器学习技术主要用于实现这些应用程序，开发人员已经发布了一系列库，使任何程序员都可以轻松地从这项技术中受益。虽然对这些发展的兴奋导致了许多积极的贡献，但也伴随着对个人数据隐私的担忧，以及这些系统歧视某些人的可能性。该项目旨在通过探索验证技术来发现导致隐私损失和歧视的受保护信息使用情况，从而提前解决这些问题。受机器学习模型中预测属性的最新进展的启发，我们建立在软件模型检查和优化的方法基础上，以定位对这些结果至关重要的组件，并构建有助于移除它们的数据表示。与此同时，我们正在更深入地了解导致这种危害的新型软件“错误”：偏见放大，这危及公平，以及可利用的数据记忆，这会带来隐私风险。我们的目标是量化现有技术可以防止这些错误发生的程度，并为专门针对这些错误的新技术的开发提供信息。随着该项目的进展，我们正在将成果应用于教育多样化的员工，使他们了解数据隐私、算法公平性以及构建有效使用机器学习的软件的严格方法。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

TEO: ephemeral ownership for IoT devices to provide granular data control

• DOI: 10.1145/3498361.3539774
• 发表时间: 2022-06
• 期刊: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services
• 作者: Han Zhang;Yuvraj Agarwal;Matt Fredrikson

Leave-one-out Unfairness

• DOI: 10.1145/3442188.3445894
• 发表时间: 2021-03
• 期刊: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Emily Black;Matt Fredrikson

Selective Ensembles for Consistent Predictions

• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Emily Black;Klas Leino;Matt Fredrikson

Consistent Counterfactuals for Deep Models

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: E. Black;Zifan Wang;Matt Fredrikson;Anupam Datta

Relaxing Local Robustness

• 发表时间: 2021-06
• 作者: Klas Leino;Matt Fredrikson