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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.

个人的生活和社会结果越来越多地由使用私人数据的多边在线平台选择和运行的不透明机器学习算法来调节。尽管这些平台经常声称他们的算法考虑到了双方的利益并保护了隐私，但这些说法并没有得到很好的定义。此外，平台的算法还针对自己的目标进行优化，例如财务或用户增长。由此产生的算法决策系统及其结果可能与平台参与者的利益和社会价值观不一致。该项目遵循由两个主要目标组成的研究议程。第一个目标是在特定平台环境中为数据共享部署差异隐私，以确保对平台参与者的严格隐私保护，同时使平台能够实现其目标。我们利用平台特定的功能来开发学习增强和安全增强的框架，用于推理和部署差异隐私。第二个研究重点调查了不透明优化的不良后果，并提出了可以编码平台参与者或社会对这种优化结果的需求的定义。然后分析算法，系统，这两个研究方向都推进了通过多方面技术实现数据驱动创新这一具有社会重要性的目标，单边平台，同时保护参与者的隐私和公平性。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进行评估，被认为值得支持和更广泛的影响审查标准。