First uniform treatment of moving objects databases, the technology that supports GPS and RFID data analysis.

We live in an age of rapid technological development. The Internet already affects our lives in many ways. Indeed, we continue to depend more, and more intrinsically, on the Internet, which is increasingly becoming a fundamental piece of societal infrastructure, just as water supply, electricity grids, and transportation networks have been for a long time. But while these other infrastructures are relatively static, the Internet is undergoing swift and fundamental change: Notably, the Internet is going mobile. The world has some 6.7 billion humans, 4 billion mobile phones, and 1.7 billion Internet users. The two most populous continents, Asia and Africa, have relatively low Internet penetration and hold the greatest potentials for growth. Their mobile phone users by far outnumber their Internet users, and the numbers are growing rapidly. China and India are each gaining about half a dozen million new phone users per month. Users across the globe as a whole increasingly embrace mobile Internet devices, with smart phone sales are starting to outnumber PC sales. Indeed, these and other facts suggest that the Internet stands to gain a substantial mobile component. This mega trend towards “mobile” is enabled by rapid and continuing advances in key technology areas such as mobile communication, consumer electronics, g- positioning, and computing. In short, this is the backdrop for this very timely book on moving objects by Xiaofeng Meng and Jidong Chen.

Applications, 2nd Edition focuses on moving object management, from the location management perspective to determining how constantly changing locations affect the traditional database and data mining technology. The book specifically describes the topics of moving objects modeling and location tracking, indexing and querying, clustering, location uncertainty, traffic-aware navigation and privacy issues, as well as the application to intelligent transportation systems. Through the book, the readers will be made familiar with the cutting-edge technologies in moving object management that can be effectively applied in LBS and transportation contexts. The second edition of this book significantly expands the coverage of the latest research on location privacy, traffic-aware navigation and uncertainty. The book has also been reorganized, with nearly all chapters rewritten, and several new chapters have been added to address the latest topics on moving objects management. Xiaofeng Meng is a professor at the School of Information, Renmin University of China; Zhiming Ding is a professor at the Institute of Software, Chinese Academy of Sciences (ISCAS); Jiajie Xu is an assistant professor at the ISCAS.

Moving Objects Databases (MOD) have gained popularity as a subject for research due to the latest developments in the positioning technologies and mobile networking. Analysis of mobility data can be used to discover and deliver knowledge that can enhance public welfare. For instance, a study of traffic patterns and congestion trends can reveal some information that can be used to improve routing and scheduling of public transit vehicles. To enable analysis of mobility data, a MOD must be published. However, publication of MOD can pose a threat to location privacy of users, whose movement is recorded in the database. A user's location at one or more time points can be publicly available prior to the publication of MOD. Based on this public knowledge, an attacker can potentially find the user's entire trajectory and learn his/her positions at other time points, which constitutes privacy breach. This public knowledge is a user's quasi-identifier (QID), i.e. a set of attributes that can uniquely identify the user's trajectory in the published database. We argue that unlike in relational microdata, where all tuples have the same set of quasi-identifiers, in mobility data, the concept of quasi-identifier must be modeled subjectively on an individual basis. In this work, we study the problem of privacy preserving publication of MOD. We conjecture that each Moving Object (MOB) may have a distinct QID. We develop a possible attack model on the published MOD given public knowledge of some or all MOBs. We develop k-anonymity model (based on classical k-anonymity), which ensures that every object is indistinguishable (with respect to its QID) from at least k-1 other objects, and show that this model is impervious to the proposed attack model. We employ space generalization to achieve MOB anonymity. We propose three anonymization algorithms that generate a MOD that satisfies the k-anonymity model, while minimizing the information loss. We conduct several sets of experiments on s.