Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques

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Features

  • Describes privacy-preserving techniques for sequences, traces, time series, and trajectories of objects moving in space and time
  • Explores complex, real-world applications in medicine, biology, the web, social networks, and mobility observation systems
  • Collects contributions from researchers in both privacy-preserving data publishing and privacy-preserving data mining
  • Gathers ongoing investigations and addresses future challenges
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.

Table of Contents

General Techniques
Anonymity Technologies for Privacy-Preserving Data Publishing and Mining, Anna Monreale, Dino Pedreschi, and Ruggero G. Pensa
Privacy Preservation in the Publication of Sparse Multidimensional Data, Manolis Terrovitis, Nikos Mamoulis, and Panos Kalnis
Knowledge Hiding in Emerging Application Domains, Osman Abul
Condensation-Based Methods in Emerging Application Domains, Yucel Saygin and Mehmet Ercan Nergiz

Traces and Streams
Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization, Keren Tan, Jihwang Yeo, Michael E. Locasto, and David Kotz
Output Privacy in Stream Mining, Ting Wang and Ling Liu

Spatio-Temporal and Mobility Data
Privacy Issues in Spatiotemporal Data Mining, Aris Gkoulalas–Divanis and Vassilios S. Verykios
Probabilistic Grid–Based Approaches for Privacy-Preserving Data Mining on Moving Object Trajectories, Győző Gidófalvi, Xuegang Huang, and Torben Bach Pedersen
Privacy and Anonymity in Location Data Management, Claudio Bettini, Sergio Mascetti, Dario Freni, X. Sean Wang, and Sushil Jajodia

Time Series
Privacy Preservation on Time Series, Spiros Papadimitriou, Feifei Li, George Kollios, and Philip S. Yu
A Segment-Based Approach to Preserve Privacy in Time Series Data Mining, Yongjian Fu and Ye Zhu

Biomedical Data
A Survey of Challenges and Solutions for Privacy in Clinical Genomics Data Mining, Bradley Malin, Christopher Cassa, and Murat Kantarcioglu
Privacy-Aware Health Information Sharing, Thomas Trojer, Cheuk-kwong Lee, Benjamin C.M. Fung, Lalita Narupiyakul, and Patrick C.K. Hung

Web Usage Data
Issues with Privacy Preservation in Query Log Mining, Ricardo Baeza-Yates, Rosie Jones, Barbara Poblete, and Myra Spiliopoulou
Preserving Privacy in Web Recommender Systems, Ranieri Baraglia, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, and Fabrizio Silvestri

Social Networks
The Social Web and Privacy: Practices, Reciprocity and Conflict Detection in Social Networks, Seda Gürses and Bettina Berendt
Privacy Issues in Online Social Networks, Barbara Carminati, Elena Ferrari, Murat Kantarcioglu, and Bhavani Thuraisingham
Analyzing Private Network Data, Michael Hay, Gerome Miklau, and David Jensen

References

Editor Bio(s)

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.