Bøger af Aris Gkoulalas-Divanis

Privacy and security risks arising from the application of different data miningtechniques to large institutional data repositories have been solely investigated by anew research domain, the so-called privacy preserving data mining. Association rulehiding is a new technique on data mining, which studies the problem of hiding sensitiveassociation rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of"e;hiding"e; sensitive association rules which due to its combinatorial nature admitsa number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently arealso presented as well as a number of computationally efficient (parallel) approachesthat alleviate time complexity problems, along with a discussion regarding unsolvedproblems and future directions. Specific examples are provided throughout this bookto help the reader study, assimilate and appreciate the important aspects of this challengingproblem. Association Rule Hiding for Data Mining is designed for researchers, professorsand advanced-level students in computer science studying privacy preserving datamining, association rule mining, and data mining. This book is also suitable forpractitioners working in this industry.

This book constitutes the refereed proceedings of the International ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, PSDML 2010, held in Barcelona, Spain, in September 2010.The 11 revised full papers presented were carefully reviewed and selected from 21 submissions. The papers range from data privacy to security applications, focusing on detecting malicious behavior incomputer systems.

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique on data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of "hiding" sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. Specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.