Research Challenges in Privacy-Aware Mobility Data Analysis and in Text Mining with Enriched Data

隐私意识移动数据分析和丰富数据文本挖掘的研究挑战

• 批准号: RGPIN-2016-03913
• 负责人: Matwin, Stan
• 金额: $ 2.77万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687203
• 关键词: Research; Challenges; Privacy; Aware; Mobility

I propose a research program combining two areas in which I have worked in the last years, i.e. Mobility Data Analysis and Privacy-Preservation Techniques. Mobility data is the data created by moving devices (e.g. cellphones, GPS, wifi) registering their presence, timestamp (and, for GPS enabled devices, their position) with antennas, receivers and routers. Mobility data is ubiquitous and its volume is growing constantly. Its importance for understanding human and animal behaviour is crucial, and therefore there is general interest in collecting and exploring this type of data for a vast range of applications, ranging from traffic and transportation, ecology, epidemiology, to safety and security. The fundamental mobility data concept is a trajectory - a sequence of points where each point consists of a geospatial coordinate set and a time stamp.**The main goal of the proposed research program is to develop Machine Learning methods for the analysis of human mobility at both coarse and fine granularity, making them privacy-preserving whenever this data represents - or can identify - individuals, or breach other confidential information. While it is well known that human mobility data presents enormous privacy challenges, I show that the same applies for ship movements, particularly for smaller recreational and fishing vessels. I list specific research tasks that collectively will provide tools for addressing mobility data in a private manner. These tasks also make realistic and interesting topics of graduate theses for students working with me. Those tasks are: dividing trajectories into semantically meaningful parts (segmentation), prediction of the next point in a trajectory (next move prediction), segment classification, clustering of trajectories and use of clustering as a privacy-oriented data representation, detection of anomalous trajectories, linking and integration of extraneous data with mobility data, and privacy models conducive to the special characteristics of mobility data.**Exploring partnerships of my labs with companies that collect and own large mobility datasets, I will focus on two main types of data: ships tracks on world's oceans available through a GPS-like AIS (Automatic Identification System) platform, and people's traces left with wifi hotspots in an urban environment. I argue that this research will have significant impact. For instance, clustering urban mobility data by speed would identify spatio-temporal cycling patterns and inform the city about the times and routes with the highest likelihood of collisions between cyclists and motorists, enabling solutions (e.g. cyclist-only lanes) at specific times of the day and the year.

我提出了一个研究计划，结合了我过去几年工作的两个领域，即移动数据分析和隐私保护技术。移动数据是由移动设备（例如手机、GPS、wifi）通过天线、接收器和路由器注册其存在、时间戳（对于支持 GPS 的设备，还包括其位置）创建的数据。移动数据无处不在，并且其数量不断增长。它对于理解人类和动物行为至关重要，因此人们普遍对收集和探索此类数据的广泛应用感兴趣，从交通和运输、生态学、流行病学到安全和保障。基本的移动数据概念是轨迹 - 一系列点，其中每个点由地理空间坐标集和时间戳组成。**拟议研究计划的主要目标是开发机器学习方法来分析人类移动性粗粒度和细粒度，每当这些数据代表或可以识别个人或违反其他机密信息时，它们都可以保护隐私。众所周知，人类移动数据带来了巨大的隐私挑战，但我表明这同样适用于船舶移动，特别是小型休闲船和渔船。我列出了具体的研究任务，这些任务共同将为以私密方式处理移动数据提供工具。这些任务也为与我一起工作的学生提供了现实而有趣的研究生论文主题。 这些任务是：将轨迹划分为语义上有意义的部分（分段）、预测轨迹中的下一个点（下一步移动预测）、分段分类、轨迹聚类以及使用聚类作为面向隐私的数据表示、异常轨迹检测、无关数据与移动数据的链接和集成，以及有利于移动数据特殊特征的隐私模型。**探索我的实验室与收集和拥有大型移动数据集的公司的合作伙伴关系，我将重点关注两种主要类型的数据：通过类似 GPS 的 AIS（自动识别系统）平台获得的世界海洋上的船舶轨迹，以及城市环境中通过 WiFi 热点留下的人们的痕迹。我认为这项研究将产生重大影响。例如，按速度对城市交通数据进行聚类可以识别时空骑行模式，并向城市通报骑行者和驾车者之间最有可能发生碰撞的时间和路线，从而在特定时间提供解决方案（例如骑行者专用车道）。日期和年份。