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Collaborative Research: SaTC: CORE: Medium: Private Model Personalization

协作研究：SaTC：核心：媒介：私人模型个性化

基本信息

批准号：
2232694
负责人：
Adam Smith
金额：
$ 45万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-15 至 2027-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232694&HistoricalAwards=false
关键词：
Collaborative Research SaTC CORE Medium

项目摘要

Modern machine learning's success has brought with it a serious challenge for privacy: it is now widely documented that the models currently in use encode individual inputs in surprising ways. Understanding how to detect such memorization, and training methods that avoid it, is a major topic of current research. However, prior investigations have focused mostly on the batch model of machine learning, in which training data are all drawn from a single underlying population.This project seeks to understand the privacy risks that arise when the training data from many populations are pooled in order to take advantage of structure that is shared across populations. For example, many individuals’ photos could be pooled to train better face recognition algorithms (even though each person is interested in a different set of faces). Such settings—called “model personalization”, “multitask learning” or “meta-learning”—provide a powerful framework for combining insights from far-flung, disparate data sources. However, their power raises fundamental questions about the extent to which the results of joint analysis violate the privacy of individual users' data. The project looks both at attacks on privacy—methods for extracting individual-level or dataset-level information from the resulting predictions or models—as well as mitigation strategies based on the now-standard, state-of-the-art framework, differential privacy. The project involves both theoretical analysis and real-world experimentation. It will inform the development of training algorithms for these complex settings and provide tools for use by companies and other research groups. This impact will be facilitated by the project team's existing collaborations with industry researchers.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.

现代机器学习的成功给隐私带来了严峻的挑战：现在广泛记录表明，当前使用的模型以令人惊讶的方式对个人输入进行编码。了解如何检测这种记忆以及避免这种记忆的训练方法是当前研究的一个主要主题。然而，先前的研究主要集中在机器学习的批处理模型上，其中训练数据全部来自单个底层人群。该项目旨在了解当来自许多人群的训练数据被汇集以利用跨人群共享的结构时所产生的隐私风险。例如，可以汇集许多人的照片来训练更好的面部识别算法（即使每个人对一组不同的面部感兴趣）。此类设置（称为“模型个性化”、“多任务学习”或“元学习”）提供了一个强大的框架，用于结合来自遥远、不同数据源的见解。然而，它们的权力引发了关于联合分析结果在多大程度上侵犯个人用户数据隐私的根本问题。该项目既着眼于对隐私的攻击——从结果预测或模型中提取个人级或数据集级信息的方法——也着眼于基于现在标准、最先进的框架、差异隐私的缓解策略。该项目涉及理论分析和实际实验。它将为这些复杂环境的训练算法的开发提供信息，并为公司和其他研究小组提供使用工具。项目团队与行业研究人员的现有合作将促进这种影响。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（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation

在持续观察下计算具有差异隐私的旋转栅门模型中的不同元素

DOI：
发表时间：
2023
期刊：
NeurIPS 2023
影响因子：
0
作者：
Kalemaj, Iden;Jain, Palak;Raskhodnikova, Sofya;Sivakumar, Satchit;Smith, Adam D
通讯作者：
Smith, Adam D

Node-Differentially Private Estimation of the Number of Connected Components

DOI：
10.1145/3584372.3588671
发表时间：
2023-04
期刊：
Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
影响因子：
0
作者：
Iden Kalemaj;Sofya Raskhodnikova;Adam D. Smith;Charalampos E. Tsourakakis
通讯作者：
Iden Kalemaj;Sofya Raskhodnikova;Adam D. Smith;Charalampos E. Tsourakakis

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian DistributionsGavin Brown and Samuel B. Hopkins and Adam D. Smith

亚高斯分布的快速、样本高效、仿射不变私有均值和协方差估计Gavin Brown、Samuel B. Hopkins 和 Adam D. Smith