Download or read book Privacy Preserving Data Mining written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2008-06-10 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Transformation Approaches for Privacy Preserving Data Mining written by Rajalaxmi R R and published by Rajalaxmi R R. This book was released on 2022-10-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Privacy Preserving Data Mining written by Jaideep Vaidya and published by Springer Science & Business Media. This book was released on 2006-09-28 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Transformation for Privacy preserving Data Mining written by Stanley Robson de Medeiros Oliveira and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Introduction to Privacy Preserving Data Publishing written by Benjamin C.M. Fung and published by CRC Press. This book was released on 2010-08-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Privacy Preserving Data Publishing written by Bee-Chung Chen and published by Now Publishers Inc. This book was released on 2009-10-14 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Handbook of Database Security written by Michael Gertz and published by Springer Science & Business Media. This book was released on 2007-12-03 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Smart Computing Techniques and Applications written by Suresh Chandra Satapathy and published by Springer Nature. This book was released on 2021-07-13 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents best selected papers presented at the 4th International Conference on Smart Computing and Informatics (SCI 2020), held at the Department of Computer Science and Engineering, Vasavi College of Engineering (Autonomous), Hyderabad, Telangana, India. It presents advanced and multi-disciplinary research towards the design of smart computing and informatics. The theme is on a broader front which focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solutions to varied problems in society, environment and industries. The scope is also extended towards the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
Download or read book Encyclopedia of Data Warehousing and Mining written by Wang, John and published by IGI Global. This book was released on 2005-06-30 with total page 1382 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Process Mining in Healthcare written by Ronny S. Mans and published by Springer. This book was released on 2015-03-12 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.
Download or read book Artificial Intelligence and Data Mining Approaches in Security Frameworks written by Neeraj Bhargava and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS Written and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library. This groundbreaking new volume: Clarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks Covers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day Contains numerous examples, offering critical solutions to engineers and scientists Presents these new applications of AI and data mining that are of prime importance to human civilization as a whole
Download or read book Data Preparation for Data Mining written by Dorian Pyle and published by Morgan Kaufmann. This book was released on 1999-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Download or read book Data Privacy Foundations New Developments and the Big Data Challenge written by Vicenç Torra and published by Springer. This book was released on 2017-05-17 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a broad, cohesive overview of the field of data privacy. It discusses, from a technological perspective, the problems and solutions of the three main communities working on data privacy: statistical disclosure control (those with a statistical background), privacy-preserving data mining (those working with data bases and data mining), and privacy-enhancing technologies (those involved in communications and security) communities. Presenting different approaches, the book describes alternative privacy models and disclosure risk measures as well as data protection procedures for respondent, holder and user privacy. It also discusses specific data privacy problems and solutions for readers who need to deal with big data.
Download or read book Privacy Aware Knowledge Discovery written by Francesco Bonchi and published by CRC Press. This book was released on 2010-12-02 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities
Download or read book Data Privacy written by Nataraj Venkataramanan and published by CRC Press. This book was released on 2016-10-03 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.
Download or read book The Elements of Big Data Value written by Edward Curry and published by Springer Nature. This book was released on 2021-08-01 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.
