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CDS&E: Collaborative Research: Private Data Analytics, Synthesis, and Sharing for Large-Scale Multi-Modal Smart City Mobility Research

CDS

基本信息

批准号：
2002985
负责人：
David Evans
金额：
$ 16.5万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002985&HistoricalAwards=false
关键词：
CDS&amp Collaborative Research Private Data

项目摘要

Given the trend towards urbanization, understanding real-time human mobility in urban areas has become increasingly important for many research areas from Mobile Networking, to Transportation/Urban Planning, Behavior Modeling, Emergency Response, to recent Pandemic Mitigation. Many analytical models have been proposed to understand human mobility based on mobility data. However, most of these data are proprietary and cannot be accessed by the research community at large. Fortunately, based on the latest expansion of urban infrastructures, such mobility data has been collected by city government agencies and some companies that are willing to share the data for social good. However, a key challenge is the privacy concern since such data usually have sensitive information and system design details for potential privacy and security issues. To address this issue, the project aims to generate realistic yet synthetic mobility data through machine learning based on the real mobility data analytics and then share these realistic synthetic data with the research community. The objective of the project is to lower the entry barriers for interdisciplinary researchers in mobility data-intensive research aimed at addressing major scientific/societal challenges related to urban mobility.The core merit of the project lies in integrating two aims, i.e., privacy-preserving data synthesis and data integration, for large-scale smart city mobility research. For the first research aim, the project plans to utilize recent advances in Generative Adversarial Networks (GANs) to enable large-scale mobility data synthesis. The goal is to achieve the individual-level release of realistic synthetic mobility data by GAN-based models targeting key characteristics of human mobility. The GAN architecture proposed has novel technical components to augment basic GAN frameworks, which optimize the fundamental trade-off between privacy (regarding removing/obfuscating sensitive mobility features) and utility (in terms of preserving non-sensitive mobility features) with long-range dependencies (in terms of repeated mobility patterns) revealed. For the second research aim, the PIs plans to perform multi-modal data integration based on aligned multi-tensor decomposition under mobility semantics. The technical approach proposed is to enable multi-modal data integration based on synthetic single-modal data for comprehensive mobility modeling with a set of machine learning techniques including novel mobility semantic learning and multi-tensor decomposition with aligned spatiotemporal granularity.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.

鉴于城市化的趋势，了解城市地区的实时人类流动性对于许多研究领域变得越来越重要，从移动的网络，到交通/城市规划，行为建模，应急响应，到最近的流行病缓解。许多分析模型已被提出来理解基于移动数据的人类移动性。然而，这些数据大多是专有的，一般研究界无法获得。幸运的是，基于城市基础设施的最新扩张，这些移动数据已经被城市政府机构和一些愿意分享数据以造福社会的公司收集。然而，一个关键的挑战是隐私问题，因为这些数据通常包含敏感信息和潜在隐私和安全问题的系统设计细节。为了解决这个问题，该项目旨在通过基于真实的移动数据分析的机器学习生成真实而合成的移动数据，然后与研究社区共享这些真实的合成数据。该项目的目标是降低跨学科研究人员在流动数据密集型研究中的准入门槛，旨在解决与城市流动相关的重大科学/社会挑战。该项目的核心价值在于整合两个目标，即，隐私保护数据合成和数据集成，用于大规模智能城市移动研究。对于第一个研究目标，该项目计划利用生成对抗网络（GAN）的最新进展来实现大规模移动数据合成。其目标是通过基于GAN的模型，针对人类移动的关键特征，实现个人层面的真实合成移动数据发布。提出的GAN架构具有新颖的技术组件来增强基本的GAN框架，其优化了隐私（关于删除/混淆敏感的移动性特征）和效用（在保留非敏感的移动性特征方面）之间的基本权衡，并揭示了长期依赖性（在重复的移动性模式方面）。对于第二个研究目标，PI计划在移动语义下基于对齐的多张量分解执行多模态数据集成。提出的技术方法是基于合成的单模态数据进行多模态数据集成，以通过一组机器学习技术进行综合移动建模，包括新颖的移动语义学习和具有对齐时空粒度的多张量分解。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（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge

TransRisk：基于转移知识的移动隐私风险预测

DOI：
10.1145/3534581
发表时间：
2022
期刊：
Wearable and Ubiquitous Technologies
影响因子：
0
作者：
Xie, Xiaoyang;Hong, Zhiqing;Qin, Zhou;Fang, Zhihan;Tian, Yuan;Zhang, Desheng
通讯作者：
Zhang, Desheng

Understanding and Mitigating Accuracy Disparity in Regression