Privacy-Preserving Data Mining PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Privacy-Preserving Data Mining PDF full book. Access full book title Privacy-Preserving Data Mining by Charu C. Aggarwal. Download full books in PDF and EPUB format.
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387709924
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387709924
Category : Computers
Languages : en
Pages : 524
Get Book Here
Book Description
Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.
Author: Stanley Robson de Medeiros Oliveira
Publisher: Library and Archives Canada = Bibliothèque et Archives Canada
ISBN:
Category : Data mining
Languages : en
Pages : 167
Get Book Here
Book Description
The sharing of data is often beneficial in data mining applications. It has been proven useful to support both decision-making processes and to promote social goals. However, the sharing of data has also raised a number of ethical issues. Some such issues include those of privacy, data security, and intellectual property rights. In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios is complex. One must not only meet privacy requirements but also guarantee valid data rnining results. This status indicates the pressing need for rethinking mechanisnis to enforce privacy safeguards without losing the benefit of mining. These mechanisms can lead to new privacy control methods to convert a database into a new one in such a waY as to preserve the main features of the original database for mining. In particular, we address the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterrns and trends from the original database. To address this challening problem, we propose a unified framework for privacy-preserving data mining that ensures that the mining process will not violate privacy up to a certain degree of security. The frarnework encompasses a family of privacy-preserving data transformation rnethods, a library of algoritImis, retrieval facilities to speed up the transformation process, and a set of metrics to evaluate the effectiveness of the proposed algorithms, in terms of information loss, and to quantify how much private information has been disclosed. Our investigation concludes that privacy-preserving data mining is to some extent possible. We demonstrate empirically and tlleoretically the practicality and feasibility of achieving privacy preservation in data mining. Our experiments reveal that our framework is efféctive, meets privacy requírements. and guarantees valid data mining results while protecting sensitive information (e.g., sensitive knowIedge and individuals' privacy).
Author: Jaideep Vaidya
Publisher: Springer Science & Business Media
ISBN: 9780387258867
Category : Computers
Languages : en
Pages : 146
Get Book Here
Book Description
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.
Author: Rajalaxmi R R
Publisher: Rajalaxmi R R
ISBN: 9782998046093
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
Recent advances in data mining techniques facilitate to explore hidden knowledge from a large volume of data. When organizations share data for mining, they may restrict confidential information and knowledge to the other organizations. To protect sensitive information before data sharing, the modern age of information processing has evolved a new research area, namely Privacy Preserving Data Mining. Data transformation methods facilitate to preserve privacy without losing the benefit of data mining. The existing studies have dealt with data transformation methods for numerical data to preserve privacy in clustering and also data sanitization approaches to hide sensitive patterns. It is essential to devise new data transformation methods for categorical data to preserve privacy in clustering. The existing data sanitization approaches are capable of removing a number of legitimate patterns while concealing sensitive patterns. They also focus exclusively on specific pattern types. Nevertheless, it is necessary to develop new data sanitization approaches to hide sensitive patterns. In this work, to begin with, sensitive categorical data protection in clustering is addressed. Two hybrid data transformation methods have been devised to transform the sensitive categorical data. Then, their effectiveness in privacy preservation and clustering accuracy are validated. It is found that iv scaling and rotation transformation method improves the privacy level and the translation and rotation transformation method provides better accuracy in clustering. Hiding sensitive association rules are implemented by concealing the frequent itemsets. It includes the concepts of non-sensitive item conflict degree, item and transaction conflict ratio. Experimental results indicate that the use of item and transaction conflict ratio reduces the legitimate itemsets missed after sanitization. The work further focuses on sanitization approaches for privacy preservation of sensitive utility itemsets. With an intention to deal with this, two data sanitization approaches are devised using transaction conflict degree and item conflict degree. The experimental results indicate that the item conflict degree improves results in terms of the legitimate itemsets lost. Privacy preservation of utility and frequent itemset is also considered and two data sanitization approaches have been developed. Based on the experimental results, it can be observed that the item conflict ratio based sanitization approach minimizes non-sensitive itemsets missed and modifications in the original database. To summarize, the research works devised data transformation approaches by which privacy was ensured while maintaining accuracy in data mining
Author: Bee-Chung Chen
Publisher: Now Publishers Inc
ISBN: 1601982763
Category : Data mining
Languages : en
Pages : 183
Get Book Here
Book Description
This book is dedicated to those who have something to hide. It is a book about "privacy preserving data publishing" -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come.
Author: Jaideep Vaidya
Publisher: Springer Science & Business Media
ISBN: 0387294899
Category : Computers
Languages : en
Pages : 124
Get Book Here
Book Description
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.
Author: Benjamin C.M. Fung
Publisher: CRC Press
ISBN: 1420091506
Category : Computers
Languages : en
Pages : 374
Get Book Here
Book Description
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. Int
Author: Michael Gertz
Publisher: Springer Science & Business Media
ISBN: 0387485333
Category : Computers
Languages : en
Pages : 579
Get Book Here
Book Description
Handbook of Database Security: Applications and Trends provides an up-to-date overview of data security models, techniques, and architectures in a variety of data management applications and settings. In addition to providing an overview of data security in different application settings, this book includes an outline for future research directions within the field. The book is designed for industry practitioners and researchers, and is also suitable for advanced-level students in computer science.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799889556
Category : Computers
Languages : en
Pages : 2188
Get Book Here
Book Description
With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data.
Author: Wang, John
Publisher: IGI Global
ISBN: 1591405599
Category : Computers
Languages : en
Pages : 1382
Get Book Here
Book Description
Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.
