| 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.
Since its inception in 2000 with two conference papers titled "Privacy Preserving Data Mining", research on learning from data that we aren't allowed to see has multiplied dramatically. Publications have appeared in numerous venues, ranging from data mining to database to information security to cryptography. While there have been several privacy-preserving data mining workshops that bring together researchers from multiple communities, the research is still fragmented.
This book presents a sampling of work in the field. The primary target is the researcher or student who wishes to work in privacy-preserving data mining; the goal is to give a background on approaches along with details showing how to develop specific solutions within each approach. The book is organized much like a typical data mining text, with discussion of privacy-preserving solutions to particular data mining tasks. Readers with more general interests on the interaction between data mining and privacy will want to concentrate on Chapters 1-3 and 8, which describe privacy impacts of data mining and general approaches to privacy-preserving data mining. Those who have particular data mining problems to solve, but run into roadblocks because of privacy issues, may want to concentrate on the specific type of data mining task in Chapters 4-7.
The authors sincerely hope this book will be valuable in bringing order to this new and exciting research area; leading to advances that accomplish the apparently competing goals of extracting knowledge from data and protecting the privacy of the individuals the data is about. |
