Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data. This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)

Computers & Technology

Wang, Ke

Yu, Philip S.

Fung, Benjamin C.M.

Fu, Ada Wai-Chee

Computer Science

Chapman and Hall/CRC

Relational Data Clustering: Models, Algorithms, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: <ul> <li>Clustering on bi-type heterogeneous relational data</li> <li>Multi-type heterogeneous relational data</li> <li>Homogeneous relational data clustering</li> <li>Clustering on the most general case of relational data</li> <li>Individual relational clustering framework</li> <li>Recent research on evolutionary clustering</li> </ul> This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Business & Money

Zhang, Zhongfei

Long, Bo

Economics

A culmination of the authors’ years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.
 After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:
 <ol>
 <li>Clustering on bi-type heterogeneous relational data</li>
 <li>Multi-type heterogeneous relational data </li>
 <li>Homogeneous relational data clustering</li>
 <li>Clustering on the most general case of relational data</li>
 <li>Individual relational clustering framework</li>
 <li>Recent research on evolutionary clustering</li>
 
</ol>
 This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. 
 The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data.
 This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.

Domain Driven Data Mining

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Cao, Longbing

Zhang, Chengqi

Zhao, Yanchang

Management & Leadership

Springer

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Broad Learning Through Fusions: An Application on Social Networks

<div>This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.</div>

Zhang, Jiawei

This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.

Differential Privacy and Applications (Advances in Information Security, 69)

Zhou, Wanlei

Zhu, Tianqing

李刚

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few&nbsp;are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. <div> </div> <div>In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning. <div> <div> <div> </div> </div> </div> </div>

Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)

Shi, Chuan

王潇

See the best price to sell, buy, or rent books by Yu, Philip S.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Yu, Philip S.

See the best price to sell, buy, or rent books by Yu, Philip S.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)

Relational Data Clustering: Models, Algorithms, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Relational Data Clustering: Models, Algorithms, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

Domain Driven Data Mining

Domain Driven Data Mining

Broad Learning Through Fusions: An Application on Social Networks

Broad Learning Through Fusions: An Application on Social Networks

Differential Privacy and Applications (Advances in Information Security, 69)

Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)