Bøger af Khaled El Emam

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic datafake data generated from real dataso you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.This book describes:Steps for generating synthetic data using multivariate normal distributionsMethods for distribution fitting covering different goodness-of-fit metricsHow to replicate the simple structure of original dataAn approach for modeling data structure to consider complex relationshipsMultiple approaches and metrics you can use to assess data utilityHow analysis performed on real data can be replicated with synthetic dataPrivacy implications of synthetic data and methods to assess identity disclosure

How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing device and IoT data, based on collection models and use cases that address real business needs. These examples come from some of the most demanding data environments, such as healthcare, using approaches that have withstood the test of time.Create anonymization solutions diverse enough to cover a spectrum of use casesMatch your solutions to the data you use, the people you share it with, and your analysis goalsBuild anonymization pipelines around various data collection models to cover different business needsGenerate an anonymized version of original data or use an analytics platform to generate anonymized outputsExamine the ethical issues around the use of anonymized data

Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets.Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others.Understand different methods for working with cross-sectional and longitudinal datasetsAssess the risk of adversaries who attempt to re-identify patients in anonymized datasetsReduce the size and complexity of massive datasets without losing key information or jeopardizing privacyUse methods to anonymize unstructured free-form text dataMinimize the risks inherent in geospatial data, without omitting critical location-based health informationLook at ways to anonymize coding information in health dataLearn the challenge of anonymously linking related datasets

Offers the tools needed for software engineers and project managers to calculate how much they should invest in quality, what benefits the investment can reap, and just how quickly those benefits can be realized. This book provides the quantitative models necessary for making real and reasonable calculations.

Elements of Software Process Assessment and Improvement reviews current assessment practices, experiences, and new research trends in software process improvement. Revised chapters expanded from articles in The Software Process Newsletter of the IEEE Computer Society Technical Council on Software Engineering, describe the improvement cycle in detail: from diagnosing an organization, establishing a business case, and changing elements within a process to final evaluation. This book's thorough examination of contemporary models evaluates an organization's processes and capabilities, covers the business argument for assessment and improvement, and illustrates expected improvements and assessment reliability methods. Additional information includes application guidelines covering critical success factors including tools and techniques and important developments that enhance the reader's understanding of organizational processes in practice.