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Linking Sensitive Data

into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers;

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Linking Sensitive Data

into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers;

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Privacy-Preserving Data Publishing

Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data;

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Privacy-Preserving Data Publishing

Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data;

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M&A and Privatisations in Poland

The author emphasises the overall complexity of cross-border M&A transactions into Poland, linking the proper M&A transaction with the;

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Linking Data

In the light of recent theoretical and methodological advances Linking Data describes the ways in which substantive problems and research;

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Linking and Aligning Scores and Scales

In this book, experts in statistics and psychometrics describe classes of linkages, the history of score linkings, data collection designs;

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AdaBoost Extensions for Cost-Sensitive Classification

This book provides a review and a research of a special type of classification technique which is known as cost-sensitive data mining. Cost;

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Incomplete Categorical Data Design

potentially inaccurate data. Addressing this difficulty, non-randomized response approaches enable sample survey practitioners and applied;

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Incomplete Categorical Data Design

potentially inaccurate data. Addressing this difficulty, non-randomized response approaches enable sample survey practitioners and applied;

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Rethinking Library Linking

). Key to understanding OpenURL is the concept of context sensitive linking: links to the same item will be different for users of different;

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Randomized Response and Related Methods: Surveying Sensitive Data

A fully updated and accessible overview of randomized response;...

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The Manipulation Of Air-Sensitive Compounds

atmosphere techniques, while Part Two treats vacuum line techniques. Appendixes provide safety data, information on materials used to construct;

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Big Data in History

, linking local data into global patterns, and exploring the results.;

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Customer Relationship Management

The coverage includes the theoretical dimensions of the topic besides linking them with industry illustrations. In addition, the text;

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Sensitive Research in Social Work

light on how to use traditional research methods sensitively, and how to generate data while minimizing the harm that can potentially be caused;

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Data Integrity in Cloud Computing Systems

In the current era of digital world, the amount of sensitive data produced by many organizations is outpacing their storage ability. The;

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IPhone Forensics

need to adequately manage sensitive data. iPhones store an enormous amount of information useful to both security professionals and malicious;

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Privacy Protecting Biometric Authentication Systems

There is an increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. The major;

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Cost-Sensitive Machine Learning

: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation;

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Cost-Sensitive Machine Learning

: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation;

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Association Rule Hiding for Data Mining

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have;

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Managing Catastrophic Loss of Sensitive Data

Offering a structured approach to handling and recovering from a catastrophic data loss, this book will help both technical and non;

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Data Science Ethics: Concepts, Techniques, and Cautionary Tales

Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for;

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Data Science Ethics: Concepts, Techniques, and Cautionary Tales

Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for;

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Pressure-Sensitive Formulation

behind certain mixing and processing technologies, is doubtless more important than a collection of compositional data and technical parameters;

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Reengineering the Survey of Income and Program Participation

disadvantages of strategies for linking administrative records and survey data, taking account of the accessibility of relevant administrative records;

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