Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified.

Autorentext

Dr. Gabriel Ghinita is an Assistant Professor with the Department of Computer Science at University of Massachusetts, Boston (UMB). Prior to joining UMB, he was a Research Associate affiliated with the Computer Science Department at Purdue University, and the Purdue Cyber Center. He holds a Ph.D. degree in Computer Science (2008) from the National University of Singapore, and a BS degree in Computer Science (2003) from Politechnica University of Bucharest. Dr. Ghinitas research interests lie in the area of databases, with focus on information security and privacy. His work includes research papers on Anonymous Publication of Geospatial, Relational and Set valued data, Privacy Preserving Sharing of Location Data, Secure Data Outsourcing and Secure Data Provenance, and Trustworthiness Assessment. He is also interested in spatio-temporal databases and data management in large-scale distributed environments. Dr. Ghinitas professional service includes participation on the Program Committee of top database conferences (ACM SIGMOD, PVLDB, ICDE), as well as reviewing for journals such as VLDBJ, IEEE TKDE, IEEE TPDS, IEEE TMC, IEEE TDSC, and GeoInformatica.

Inhalt
Introduction.- Privacy-Preserving Spatial Transformations.- Cryptographic Approaches.- Hybrid Approaches.- Private Matching of Spatial Datasets.- Trajectory Anonymization.- Differentially Private Publication of Spatial Datasets.- Conclusions.