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CAREER: Algorithms, Incentives, and Policy for Data Privacy

职业：数据隐私的算法、激励和政策

基本信息

批准号：
1942772
负责人：
Rachel Cummings
金额：
$ 48.89万
依托单位：
Georgia Tech Research Corporation
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 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942772&HistoricalAwards=false
关键词：
CAREER Algorithms Incentives Policy Data

项目摘要

Privacy concerns are becoming a major obstacle to using data, and it is often unclear how current regulations should translate into technology. As differential privacy and other privacy tools are brought to bear in practice, new challenges arise in ensuring these applications maintain the privacy guarantees intended by theory. How, then, should organizations utilize advances in privacy technologies to make use of potentially sensitive data? This project bridges the gap between theory and practice in the formal study of privacy by addressing new technical challenges that arise when theoretical privacy technologies are implemented in real-world settings.This project addresses three main technical questions: Firstly, how should a data curator optimally allocate a fixed differential privacy budget across multiple analysis tasks, to trade off the value of accurate analysis with the privacy budget? Secondly, how do people reason about and value their privacy in practice, and how do these valuations change based on context of the data or analysis task? Thirdly, how should markets for personal data be regulated to protect individual privacy and population-level fairness, while still enabling valuable data-driven decision making? Answering these questions requires an interdisciplinary approach that integrates tools from computer science for differentially private algorithm design, economics to understand incentives of organizations and individuals, and public policy for regulation of privacy technologies. To ensure the broad impact of this research, this project also includes a significant educational and outreach component, including curriculum development, mentorship of students, and workshop organization aimed at improving diversity in graduate education.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.

对隐私的担忧正成为使用数据的主要障碍，而且人们往往不清楚当前的监管应如何转化为技术。随着差分隐私和其他隐私工具在实践中的应用，在确保这些应用程序保持理论所期望的隐私保障方面出现了新的挑战。那么，组织应该如何利用先进的隐私技术来利用潜在的敏感数据呢？该项目通过解决理论隐私技术在现实世界中实施时出现的新技术挑战，弥合了隐私正式研究中理论与实践之间的差距。该项目解决了三个主要的技术问题：首先，数据管理员应该如何在多个分析任务之间最佳地分配固定的差异隐私预算，以权衡准确分析的价值和隐私预算？其次，人们在实践中如何对自己的隐私进行推理和评估，这些评估如何根据数据或分析任务的背景而变化？第三，如何监管个人数据市场，以保护个人隐私和人口层面的公平，同时仍能实现有价值的数据驱动决策？回答这些问题需要一种跨学科的方法，将不同隐私算法设计的计算机科学工具、理解组织和个人激励的经济学工具以及隐私技术监管的公共政策工具整合在一起。为了确保这项研究的广泛影响，该项目还包括一个重要的教育和推广组成部分，包括课程开发、学生指导和旨在提高研究生教育多样性的研讨会组织。该奖项反映了美国国家科学基金会的法定使命，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

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

What are the chances? Explaining the epsilon parameter in differential privacy

有什么机会？

DOI：
发表时间：
2023
期刊：
Proceedings of the 32nd USENIX Conference on Security Symposium
影响因子：
0
作者：
Nanayakkara, Priyanka;Smart, Mary Anne;Cummings, Rachel;Kaptchuk, Gabriel;Redmiles, Elissa M.
通讯作者：
Redmiles, Elissa M.

The Privacy Elasticity of Behavior: Conceptualization and Application

隐私行为弹性：概念化与应用

DOI：
10.1145/3580507.3597778
发表时间：
2023
期刊：
ACM
影响因子：
0
作者：
Dekel, Inbal;Cummings, Rachel;Heffetz, Ori;Ligett, Katrina
通讯作者：
Ligett, Katrina

Centering Policy and Practice: Research Gaps Around Usable Differential Privacy

DOI：
10.1109/tps-isa58951.2023.00024
发表时间：
2023-11
期刊：
2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
影响因子：
0
作者：
Rachel Cummings;Jayshree Sarathy
通讯作者：
Rachel Cummings;Jayshree Sarathy

Mean Estimation with User-level Privacy under Data Heterogeneity