Account
Orders
Advanced search
Techniques, Challenges, and Solutions for Data Privacy Across Multiple Sectors
Louise Reader
Read on Louise Reader App.
This book is a thorough and practical guide to minimizing personally identifiable information (PII) in every conceivable use case across Finance, Healthcare, Insurance, Legal, Marketing, HR, and Government.
Most data protection laws and regulations require that businesses only use as much PII as is required for each specific processing purpose. In some cases, processing is only permitted when the data is fully anonymized. Hence, PII Minimization describes a spectrum from redacting very few, if any, direct identifiers to full anonymization.
It is woefully unclear what exactly is required in terms of PII minimization. The feasibility and the degree of PII minimization crucially depend on what personal identifiers are present in the data set to be processed as well as the use case for processing it.
Industry- and use-case-specific PII-Minimization Standards supplies expert insights from academia as well as the seven industries to be covered. These experts clarify what personal identifiers are commonly present in the data sets collected by or otherwise available to them, what use cases for data processing are prevalent in their industry, and which personal identifiers are (un)necessary for each use case.
The book also features companies that are developing technological solutions to solve the difficult problem of data minimization. The practical insights to be gained here are how to achieve data minimization in specific use cases and with high accuracy to meet the regulatory requirements. As an example, for the development of facial recognition software, images of human faces must be used in machine-identifiable form. However, today’s technology can modify facial images for other use cases in such a way that they remain identifiable by human viewers but prevent the identification by automated systems.
You Will:
Who This Book is for:
Data protection regulators as well as risk officers, privacy and data protection officers, product leaders, cybersecurity officers, information officers, and data leaders within organizations operating in Finance, Healthcare, Insurance, Legal, Marketing, HR, and Government that collect or process PII for purposes that require certain personal identifiers to be removed or obfuscated to meet data minimization requirements. The book is also for regulators developing actionable data minimization standards for these seven industries.
Les livres numériques peuvent être téléchargés depuis l'ebookstore Numilog ou directement depuis une tablette ou smartphone.
PDF : format reprenant la maquette originale du livre ; lecture recommandée sur ordinateur et tablette EPUB : format de texte repositionnable ; lecture sur tous supports (ordinateur, tablette, smartphone, liseuse)
DRM Adobe LCP
LCP DRM Adobe
This ebook is DRM protected.
LCP system provides a simplified access to ebooks: an activation key associated with your customer account allows you to open them immediately.
ebooks downloaded with LCP system can be read on:
Adobe DRM associates a file with a personal account (Adobe ID). Once your reading device is activated with your Adobe ID, your ebook can be opened with any compatible reading application.
ebooks downloaded with Adobe DRM can be read on:
mobile-and-tablet To check the compatibility with your devices,see help page
Patricia Thaine is the Co-Founder & Chairwoman of Private AI, a Microsoft-backed company whose technology enables organizations to detect, understand, redact, and anonymize their data at scale. Her R&D work focuses on privacy-preserving natural language processing, applied cryptography, re-identification risk, and maximizing the utility of data for AI and machine learning, the core challenges at the heart of PII minimization.
Patricia was named a 2023 Technology Pioneer by the World Economic Forum, and Private AI was recognized as a Gartner Cool Vendor in Privacy and won the Privacy Innovation Award at PICCASO 2024. She is a Vector Institute alumna, the co-inventor of a US patent, and brings over a decade of research and software development experience. Patricia hosts The Data Frontier podcast and was named to Maclean's Power List 2024 as one of the top 100 Canadians shaping the country.
Kathrin Gardhouse is Private AI's former Privacy Evangelist. She has since shifted into AI policy research with a focus on the EU and Canada. As a Senior AI Governance Associate at The Future Society and the Policy Lead of AI Governance and Safety Canada, she advises policymakers on risks from general-purpose and agentic AI and appropriate regulatory responses. As a Summer Research Fellow, she contributed to a legal commentary to the EU AI Act by the Institute for Law and AI, writing about the AI Office's enforcement powers. Kathrin is a lawyer by training and holds a philosophy PhD from McMaster University. She is certified by the IAPP as an Information Protection and AI Governance Professional.
Sign up to get our latest ebook recommendations and special offers