Detection and surveillance technologies: privacy-related requirements and protection schemes
International Journal of Electronic Security and Digital Forensics
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Edit constraints on microaggregation and additive noise
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
“Secure” log-linear and logistic regression analysis of distributed databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Emphasizing ethics and privacy preservation in an undergraduate data mining course
Journal of Computing Sciences in Colleges
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
Privacy-preserving ranking over vertically partitioned data
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense. Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area. Privacy Preserving Data Mining is designed for a professional audience composed of practitioners and researchers in industry. This volume is also suitable for graduate-level students in computer science.