Privacy-Aware Knowledge Discovery Novel Applications and New Techniques
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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.
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
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
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
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
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