CRII: SaTC: Democratizing Differential Privacy via Algorithms for Hybrid Models

CRII：SaTC：通过混合模型算法使差异隐私民主化

基本信息

批准号：
1755992
负责人：
Aleksandra Korolova
金额：
$ 17.5万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755992&HistoricalAwards=false
关键词：
CRII SaTC Democratizing Differential Privacy

项目摘要

Individuals generate enormous amounts of personal data that are subsequently collected and stored by organizations and governments. The data powers many innovative applications in areas such as web services, health care, and transportation, but they also increase privacy risks. Differential privacy, a framework to rigorously reason about privacy properties of algorithms, holds tremendous promise for enabling privacy-preserving yet useful data analyses. However, its adoption has been limited to entities with massive user bases. This project aims to democratize the ability to deploy differential privacy by making it practical for entities with smaller user bases. Research activities in this project include formulation of a new, hybrid, privacy model that models heterogeneous privacy preferences of individuals and current industry practices. The project also develops novel algorithms that preserve differential privacy while taking advantage of the hybrid model to improve utility outcomes, and evaluation of their performance. The work makes progress towards eliminating one of the significant barriers to data-driven innovation by expanding the applicability of differential privacy to a wider range of entities.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.

个人产生大量的个人数据，随后由组织和政府收集和存储。数据为Web服务，医疗保健和运输等领域的许多创新应用提供了动力，但它们也增加了隐私风险。差异隐私是算法隐私属性的严格理由的框架，对实现隐私而有用但有用的数据分析具有巨大的希望。但是，它的采用仅限于具有大量用户基础的实体。该项目旨在通过使用户群较小的实体实用的实用性来使部署差异隐私的能力民主化。该项目中的研究活动包括制定一种新的混合，隐私模型，该模型模拟了个人和当前行业实践的异质隐私偏好。该项目还开发了新颖的算法，这些算法可以保留差异隐私，同时利用混合模型来改善效用结果并评估其性能。这项工作通过扩大差异隐私对更广泛的实体的适用性来消除数据驱动创新的重要障碍之一。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。