NeTS: Small: Geometric and Topological Analysis on Trajectory Sensing: Collection, Classification and Anonymization

NeTS：小型：轨迹感知的几何和拓扑分析：收集、分类和匿名化

• 批准号: 1618391
• 负责人: Jie Gao
• 金额: $ 25万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618391&HistoricalAwards=false
• 关键词: NeTS; Small; Geometric; Topological; Analysis

The maturing of mobile devices and systems provide an unprecedented opportunity to collect a large amount of data about real world human motion at all scales. The rich knowledge contained in these data sets can have a huge impact in many fields ranging from transportation to health care, from civil engineering to energy management, from e-commerce to social networking. While the applications are paradigm-transforming, recent studies show that the trajectory data can raise serious privacy concerns in revealing personally sensitive information such as frequently visited locations or social ties. These concerns become the major hurdle in utilizing these data sets. This project systematically studies the issue of anonymizing trajectory data, from the bottom layer of trajectory sensing and data collection, to the middle layer of trajectory representation and anonymity, to the application layer of how the anonymized trajectory data can be used.By the nature of trajectories as being time stamped sequence of points, in this project novel geometric and topological algorithms that directly work on distributed sensors collecting the trajectories are developed for achieving the objective. Queries to such decentralized sensors are made to ensure no sensitive information is released. The intellectual contribution lies in the following aspects. 1) The topological representation of trajectories, i.e., how trajectories pass around obstacles and landmarks in the domain is adopted. The topological representation is compact and descriptive, introducing novel discrete and combinatorial problems to study. 2) A novel framework is developed for distributed sensors to directly learn, classify and compare the topological types of the target trajectories, using harmonic one-forms and Hodge decomposition from algebraic topology. The new framework can substantially reduce the communication cost within the network, while maintaining the requirement of user privacy from the very beginning of sensing and data collection. 3) A family of anonymization algorithms using different ideas are developed, by altering the way to connect the time-stamped points into trajectories, by adjusting the topological resolution to reach a balance between data anonymity and utility, and by sensing and recording randomized hash data to answer popular trajectory queries. 4) The trajectory data sets are often huge, so algorithms for handling large scale trajectory data sets are developed in both centralized and decentralized settings.

移动设备和系统的成熟提供了一个前所未有的机会，可以在所有规模上收集有关现实世界运动的大量数据。这些数据集中包含的丰富知识可能会对从运输到医疗保健的许多领域产生巨大影响，从土木工程到能源管理，从电子商务到社交网络。尽管应用程序是范式转换的，但最近的研究表明，轨迹数据可能会引起严重的隐私问题，以揭示个人敏感信息，例如经常访问的位置或社交关系。这些担忧成为利用这些数据集的主要障碍。 该项目系统地研究了匿名轨迹数据的问题，从轨迹感应和数据收集的底层到轨迹表示和匿名的中间层，再到如何使用匿名轨迹数据的应用层。通过轨迹的性质。作为时间的分配型号，这些轨迹作为时间的分配型号，这些轨迹是在此项目中直接收集的轨迹，这些轨迹是在该项目中直接进行的，这些轨迹直接在轨迹上进行了分配的轨迹，这些轨迹是在轨迹上进行分配的，这些轨迹的轨迹是在进行的轨迹，这些轨迹的轨迹是在进行的轨迹，这些轨迹的轨迹是在进行的轨迹。 客观的。对此类分散传感器进行查询，以确保没有发布敏感信息。智力贡献在于以下方面。 1）轨迹的拓扑表示，即轨迹如何通过域中的障碍和地标。拓扑表示是紧凑而描述性的，引入了新的离散和组合问题以进行研究。 2）为分布式传感器开发了一个新的框架，以直接学习，分类和比较目标轨迹的拓扑类型，并使用代数拓扑的谐波单形和霍奇分解。新框架可以大大降低网络内的通信成本，同时从感应和数据收集开始时保持用户隐私的要求。 3）通过调整拓扑分辨率以在数据匿名和效用之间达到平衡，并通过感应和记录随机的哈希数据来回答流行的轨迹查询，从而开发了使用不同想法的匿名算法使用不同想法的算法。 4）轨迹数据集通常是巨大的，因此用于处理大规模轨迹数据集的算法都是在集中式和分散设置中开发的。