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.

我提出了一项研究计划，结合了我过去几年中工作的两个领域，即移动性数据分析和隐私保护技术。移动性数据是通过移动设备（例如手机，GPS，WIFI）来创建的数据，该数据将其存在时间戳（以及启用GPS的设备，其位置）使用天线，接收器和路由器创建的数据。流动性数据无处不在，其体积不断增长。它对于理解人类和动物行为的重要性至关重要，因此，对收集和探索此类数据的广泛应用具有一般兴趣，从交通和运输，生态学，流行病学到安全和安全。基本的活动能力数据概念是一种轨迹 - 每个点由地理空间坐标集和时间戳记组成。众所周知，人类流动性数据提出了巨大的隐私挑战，但我表明，船舶运动也适用，特别是对于较小的娱乐船和渔船。我列出了特定的研究任务，这些任务将共同提供以私人方式解决移动性数据的工具。这些任务还为与我一起工作的学生提供了逼真而有趣的主题。 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我的实验室与收集和拥有大型移动性数据集的公司建立了合作伙伴关系，我将重点关注两种主要数据类型：通过GPS样AIS（自动标识系统）平台获得世界海洋的船只轨道，以及在城市环境中留下WiFi Hotspots的人们的痕迹。我认为这项研究将产生重大影响。例如，按速度群集的城市流动性数据将确定时空循环模式，并将骑自行车者与驾车者之间碰撞可能性最高的时间和路线告知城市，从而在一天中和一年中的特定时间启用解决方案（例如，仅骑自行车的人）。