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

我提出了一个研究计划，结合了我在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。移动性数据是移动设备(如手机、GPS、WiFi)通过天线、接收器和路由器记录它们的存在、时间戳(对于支持GPS的设备，以及它们的位置)所创建的数据。移动数据无处不在，数据量也在不断增长。它对于理解人类和动物行为至关重要，因此，人们普遍有兴趣收集和探索这种类型的数据，用于广泛的应用，从交通和运输、生态学、流行病学到安全和安保。基本的移动性数据概念是轨迹--其中每个点由地理空间坐标集和时间戳组成的点序列。 拟议研究计划的主要目标是开发机器学习方法，用于分析粗粒度和细粒度的人类流动性，使其在这些数据代表或可以识别个人或违反其他机密信息时保护隐私。虽然众所周知，人员流动数据带来了巨大的隐私挑战，但我指出，同样的情况也适用于船只移动，特别是对较小的休闲和渔船。我列出了具体的研究任务，这些任务将共同提供以私人方式处理移动数据的工具。对于和我一起工作的学生来说，这些任务也使毕业论文成为现实和有趣的话题。这些任务是：将轨迹划分为具有语义意义的部分(分段)、轨迹中下一点的预测(下一步预测)、分段分类、轨迹的聚类和使用聚类作为面向隐私的数据表示、异常轨迹的检测、无关数据与移动性数据的链接和整合、以及有利于移动性数据的特殊特征的隐私模型。 在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时，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)平台获得世界海洋上的船舶轨迹，以及城市环境中WiFi热点留下的人们的踪迹。我认为这项研究将产生重大影响。例如，根据速度对城市机动性数据进行聚类将识别时空骑行模式，并向城市通报骑车者和驾车者之间发生碰撞的可能性最高的时间和路线，从而在一天和一年的特定时间段实现解决方案(例如，自行车专用道)。

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.