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

CDS

• 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& Collaborative; Research; Private; Data

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从移动网络到运输/城市规划，行为建模，应急响应再到最近大流行缓解的许多研究领域的实时人类流动性变得越来越重要。已经提出了许多分析模型，以理解基于移动性数据的人类流动性。但是，这些数据中的大多数都是专有的，研究界无法访问。幸运的是，根据城市基础设施的最新扩展，这类出行数据是由市政府机构和一些愿意以社会利益共享数据的公司收集的。但是，一个主要的挑战是隐私问题，因为此类数据通常具有敏感的信息和系统设计细节，以解决潜在的隐私和安全问题。为了解决这个问题，该项目旨在通过机器学习基于实际移动性数据分析来生成现实但合成的移动性数据，然后与研究社区共享这些现实的合成数据。该项目的目的是降低跨学科研究人员在流动性数据密集型研究中的进入障碍，旨在应对与城市流动性相关的主要科学/社会挑战。该项目的核心优点在于将两个目标集成，即具有隐私性的具有隐私性数据综合和数据整合的目标，以实现大型智能城市流动性研究。对于第一个研究目的，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的移动性数据综合。目的是通过针对人类移动性的关键特征的基于GAN的模型来实现现实合成移动性数据的个体释放。拟议的GAN架构具有新颖的技术组件来增强基本的GAN框架，从而优化了隐私之间的基本权衡（有关删除/混淆敏感的移动性功能）和实​​用程序（就保留不敏感的移动性特征而言），以长期依赖性（在重复的移动性模式方面）揭示了隐私框架。对于第二个研究目的，PIS计划基于在移动性语义下基于对齐的多张量分解进行多模式数据集成。提出的技术方法是基于合成单模式数据启用多模式数据的整合，以通过一组机器学习技术进行全面的移动性建模，包括新型流动性语义学习和多张tensor分解，并具有一致的时空粒度性。这一奖项反映了NSF的法定任务和综述的依据，这是通过评估的范围来进行的。

项目成果

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
• 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

• 发表时间: 2021-02
• 期刊: ArXiv
• 作者: Jianfeng Chi;Yuan Tian;Geoffrey J. Gordon;Han Zhao

Towards Return Parity in Markov Decision Processes

• 发表时间: 2021-11
• 作者: Jianfeng Chi;Jian Shen;Xinyi Dai;Weinan Zhang;Yuan Tian;Han Zhao

Model-Targeted Poisoning Attacks with Provable Convergence

具有可证明收敛性的模型目标中毒攻击

• 发表时间: 2021
• 期刊: 38th International Conference on Machine Learning
• 作者: Suya, Fnu;Mahloujifar, Saeed;Suri, Anshuman;Evans, David;Tian, Yuan
• 通讯作者: Tian, Yuan

CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU

• DOI: 10.1109/sp40001.2021.00098
• 发表时间: 2021-04
• 期刊: 2021 IEEE Symposium on Security and Privacy (SP)
• 作者: Sijun Tan;Brian Knott;Yuan Tian;David J. Wu