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

基本信息

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

项目摘要

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，支撑着收集广泛个人信息以提供新颖的功能和有价值的见解的系统和服务，机器学习技术主要用于实现这些应用程序，并且开发人员已经发布了一系列库来实现这些应用程序。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带来了许多积极的贡献，但也伴随着对个人数据隐私以及这些系统可能歧视某些个人的担忧。该项目旨在通过探索验证来解决这些问题揭示导致隐私丢失和歧视的受保护信息使用实例的技术受到机器学习模型中预测归因的最新进展的启发，我们基于软件模型检查和优化的方法来定位对这些结果至关重要的组件，并构建。与此同时，我们正在对导致此类危害的新型软件“错误”有更深入的了解：偏见放大会危及公平性，而可利用的数据记忆则会带来隐私风险。量化现有技术可以在多大程度上防止这些错误的发生，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，我们正在应用这些结果，对多元化的员工进行数据隐私、算法方面的教育。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
Han Zhang;Yuvraj Agarwal;Matt Fredrikson
通讯作者：
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
影响因子：
0
作者：
Emily Black;Matt Fredrikson
通讯作者：
Emily Black;Matt Fredrikson

Relaxing Local Robustness