Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.
This volume focuses on the investigatory methods applied to autosomal dominant polycystic kidney disease (ADPKD), one of the most common human genetic diseases. ADPKD is caused by mutations in PKD1 and TRPP2, two integral membrane proteins that function as receptor/ion channels in primary cilia of tubular epithelial cells. Thus, ADPKD belongs to ciliopathies, a group of disorders caused by abnormal cilia formation or function. This proposed book will cover the state-of-the-art methods ranging from molecular biology, biochemistry, electrophysiology, to tools in model animal studies. Key Features Explores the role of cilia in polycystic kidney disease Focuses on myriad state-of-the-art methods and techniques Reviews specific mutations integral to this autosomal genetic disease Includes discussions of model systems
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on image classi?cation applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Summarizes the whole field of adversarial robustness for Machine learning models Provides a clearly explained, self-contained reference Introduces formulations, algorithms and intuitions Includes applications based on adversarial robustness
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