With the growing market of sensing and positioning technologies and the growing popularity and availability of mobile communications, location-based information management has become an important problem in mobile computing systems. Furthermore, the computational capabilities in mobile devices, ranging from navigational systems in cars to hand-held devices and cell phones, continue to rise, making mobile devices increasingly accessible. However, significant research efforts to date have been devoted to location management techniques and location-based services in centralized location monitoring systems. Very few have studied the distributed approach to real-time location monitoring. We argue that for mobile applications that need to manage a large and growing number of mobile objects, the centralized approaches do not scale well in terms of server load and network bandwidth, and are vulnerable to single point of failure. 
This tutorial presents the necessary concepts, architectures, techniques, and infrastructure to understand Location-based Services in mobile information systems. The tutorial is designed to be self-contained, and gives the essential background for anyone planning to learn about and contribute to the principles and applications of location-based services in mobile commerce and geographical information systems. It guides practitioners by highlighting best practices in location based information monitoring and introduces students and advanced developers to design and engineering issues in building scalable and privacy-aware distributed location based services, including the key trade-offs, as well as the limitations of current approaches. This tutorial is presented at a senior or beginning graduate student level. It is accessible to Web programmers, advanced application developers, and graduate students.

Ling Liu is an associate professor in the College of Computing at Georgia Tech. Her research involves both experimental and theoretical study of distributed data intensive systems, including distributed middleware, advanced Internet systems and Internet data management. Dr. Liu has published more than 150 articles in international journals and international conferences. She is the PC co-Chair of IEEE 2006 International Conference on Data Engineering (ICDE 2006), the vice chair of IEEE 2006 International Conference on Distributed Computing Systems (ICDCS 2006), the co-general chair of IEEE ICDE 2007. Dr. Liu is on the editorial board of several International Journals, including TKDE, VLDB Journal. Her current research is partially funded by government grants from NSF, DARPA, DoE and industry grants from IBM and HP.

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Over the last decade the quest for collecting vast amount of data on people clearly indicates the necessity to protect people’s privacy. This concern becomes even more important and evident as data becomes easily accessible over the Web or is managed by powerful database management systems.
This tutorial addresses the many facets of privacy when dealing with database systems. These issues include protecting queries and data values from being revealed to third parties by any means, avoiding information leakage that could be used for inferring protected data, and taking counter measure when data might be in danger when being managed by third parties (application providers). The literature provides a large variety of concepts and approaches that all contribute to protecting the privacy of people (and the data describing them).
This tutorial first reviews different goals of privacy preserving approaches and discusses “adversary models”, i.e. approaches that reveal data to unauthorized third parties by different means. Those adversary models heavily determine the kind of preventive measures that are necessary in order to protect data from being revealed. We then presented the different solutions in the privacy area and discuss how solutions could be used in the real world: access control, the use of encryption, the concept of k-anonymity, and different privacy preserving algorithms for database systems.

Johann-Christoph Freytag is currently full professor for databases and information systems at the Computer Science Department of the Humboldt-Universitaet zu Berlin, Germany. Before joining the department in 1994 he was a research staff member at the IBM Almaden Research Center (1985-1987), a researcher at the European Computer-Industry-Research Centre (ECRC, Munich, Germany, 1987-1989), and the head of Digital's Database Technology Center (also in Munich, 1990-1993). He holds a Ph.D. in Applied Mathematics/Com­puter Science from Harvard University, MA.
Dr. Freytag's research interests include all aspects of query processing and query optimization in object-relational database systems, new developments in the database area (such as semi-structured data, data quality, databases and security), privacy in database systems, and applying database technology to applications such as GIS, genomics,and bioinformatics/life science. During last years he received the IBM Faculty Award 4 times for collaborative work in the areas of databases, middleware, and bioinformatics/life science. Currently, he is a member of the VLDB Endowment and the head of the German database interest group of the German Computer Society GI (Gesellschaft für Informatik).
For more information please visit his web site http://www.dbis.informatik.hu-berlin.de/~freytag/.

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Privacy is becoming an increasingly important issue in many data mining applications. A considerable amount of work on privacy preserving data mining has been investigated recently. In the first part of the tutorial, we survey recent proposed approaches on randomization based privacy preserving data mining: the additive noise based, the random rotation based, and the condensation based perturbation approaches.
In the second part of the tutorial, we will discuss the limitations of each above approach and show the potential attacking methods which may be exploited by attackers to breach individual privacy. More specifically, we will present reconstruction techniques based on Spectral Filtering and Principle Component Analysis from the additive noise randomized data. For the random rotation based perturbation approach, we will show how an Independent Component Analysis based reconstruction method can be used on the random rotation perturbed data to breach privacy when a (small) subset of sample data are a-priori known by attackers.

Dr. Xintao Wu is an Assistant Professor of Computer Science Department at University of North Carolina at Charlotte. He got his Ph.D. in Information Technology from George Mason University in August 2001. He received his BS degree in Information Science from the University of Science and Technology of China in 1994, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997. His major research interests include data mining and knowledge discovery, data privacy, and data warehousing. His research has been supported by NSF and Junior Faculty Research Grants from UNC Charlotte. He is the author of numerous publications on data mining and data privacy. He has served as program committee members for the major data mining conferences: ICDM, SDM, and PAKDD. Dr. Wu is a recipient of NSF CAREER Award in 2006.

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