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Covering research at the frontier of this field, this volume presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, which face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations cover well-established results as well as explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. The book focuses on intricate, real-world applications in medicine, biology, the web, social networks, and mobility observation systems.
List of contents
General Techniques. Traces and Streams. Spatio-Temporal and Mobility Data. Time Series. Biomedical Data. Web Usage Data. Social Networks. References.
About the author
Francesco Bonchi is a senior research scientist at Yahoo! Research in Barcelona, Spain, where he is part of the Barcelona Social Mining Group. He is program co-chair of the upcoming European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010). Dr. Bonchi has also served as program co-chair of the first and second ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007 and 2008), the first IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2006), and the fourth International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005). He earned his Ph.D. in computer science from the University of Pisa.
Elena Ferrari is a professor of computer science at the University of Insubria in Italy, where she heads the Database & Web Security Group. In 2009, Dr. Ferrari received the IEEE Computer Society’s prestigious Technical Achievement Award for "outstanding and innovative contributions to secure data management." She has served as program co-chair of the third IFIP WG 11.11 International Conference on Trust Management (IFIPTM 2009), PinKDD 2007 and 2008, the fourth ACM Symposium on Access Control Models and Technologies (SACMAT 2004), and the first Workshop on Web Security and Semantic Web at COMPSAC 2002. She earned her Ph.D. in computer science from the University of Milano. Check out Dr. Ferrari's interview with the IEEE Computer Society.
Summary
Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results—they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development.
Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.
While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.
