CAREER: Towards Privacy and Fairness in Multi-Sided Platforms

职业：在多边平台中实现隐私和公平

基本信息

批准号：
1943584
负责人：
Aleksandra Korolova
金额：
$ 55万
依托单位：
University of Southern California
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 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943584&HistoricalAwards=false
关键词：
CAREER Towards Privacy Fairness Multi

项目摘要

Individuals’ lives and societal outcomes are increasingly mediated by opaque machine learning algorithms chosen and run by multi-sided online platforms using private data. Although the platforms often claim that their algorithms take into consideration the interests of the sides and preserve privacy, these claims are not well-defined. Furthermore, the platforms’ algorithms also optimize for their own objectives, such as financial or user growth. The resulting algorithmic decision-making systems and their outcomes may be at odds with the interests of platform participants and societal values.The project is following a research agenda consisting of two main thrusts. The first aims to enable the deployment of differential privacy for data sharing in platform-specific contexts, so as to ensure rigorous privacy protections for platform participants while enabling the platform to pursue its objectives. We take advantage of platform-specific capabilities to develop learning-augmented and security-augmented frameworks for reasoning about and deploying differential privacy. The second research thrust investigates undesirable consequences of opaque optimizations and proposes definitions that could encode platform participants’ or societal desiderata regarding the outcomes of such optimization. It then analyzes algorithmic, systems, and policy approaches for achieving them and quantitatively evaluates the impact of enforcing such constraints.Both research thrusts advance the societally important goals of enabling data-driven innovation by multi-sided platforms while preserving privacy and fairness for their participants.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.

个人的生活和社会成果越来越多地通过使用私人数据选择和运行的多边在线平台选择和运行的不透明机器学习算法来介导。尽管这些平台经常声称其算法考虑到方面的利益并保留隐私，但这些主张尚未明确定义。此外，平台的算法还针对自己的目标（例如财务或用户增长）进行了优化。由此产生的算法决策系统及其结果可能与平台参与者和社会价值观的利益相抵触。该项目遵循由两个主要推力组成的研究议程。第一个旨在使在平台特定环境中的数据共享中部署差异隐私，以确保对平台参与者进行严格的隐私保护，同时使平台实现其目标。我们利用特定于平台的功能来开发以学习效果和安全性的框架来推理和部署差异隐私。第二项研究推力研究了不透明的优化和建议定义的不太明显后果，这些定义可以编码平台参与者或社会渴望，以了解这种优化的结果。然后，它分析算法，系统和政策方法来实现它们并进行数量评估执行此类约束的影响。任何一项研究都推进了促进社会上重要的目标，可以通过多边平台来实现数据驱动的创新，同时通过对参与者进行了支持，并以nsf的构建范围来维护nsf的宣传。更广泛的影响审查标准。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest

拥有隐私蛋糕并把它吃掉：平台支持的社交媒体算法审计以维护公共利益

DOI：
10.1145/3579610
发表时间：
2023
期刊：
Proceedings of the ACM on Human-Computer Interaction
影响因子：
0
作者：
Imana, Basileal;Korolova, Aleksandra;Heidemann, John
通讯作者：
Heidemann, John

Pushing the Boundaries of Private, Large-Scale Query Answering

DOI：
10.48550/arxiv.2302.04833
发表时间：
2023-02
期刊：
ArXiv
影响因子：
0
作者：
Brendan Avent;A. Korolova
通讯作者：
Brendan Avent;A. Korolova

Auditing for Discrimination in Algorithms Delivering Job Ads

审核招聘广告算法中的歧视行为

DOI：
10.1145/3442381.3450077
发表时间：
2021
期刊：
Proceedings of the Web Conference (WWW
影响因子：
0
作者：
Imana, Basileal;Korolova, Aleksandra;Heidemann, John
通讯作者：
Heidemann, John

Advances and Open Problems in Federated Learning

DOI：
10.1561/2200000083
发表时间：
2021-01-01
期刊：
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
影响因子：
32.8
作者：
Kairouz, Peter;McMahan, H. Brendan;Zhao, Sen
通讯作者：
Zhao, Sen

What is an Algorithms Course?: Survey Results of Introductory Undergraduate Algorithms Courses in the U.S.

什么是算法课程？：美国本科算法入门课程的调查结果

DOI：
10.1145/3545945.3569820
发表时间：
2023
期刊：
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education
影响因子：
0
作者：
Luu, Michael;Ferland, Matthew;Nagaraj Rao, Varun;Arora, Arushi;Huynh, Randy;Reiber, Frederick;Wong-Ma, Jennifer;Shindler, Michael
通讯作者：
Shindler, Michael