Pii Minimization Handbook - Thaine, Patricia; Gardhouse, Kathrin
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Erscheint vorauss. 22. Dezember 2025
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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…mehr

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Produktbeschreibung
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: * Explore the range of techniques for minimizing PII, from basic data reduction strategies to complete anonymization. * Examine AI-specific regulations and their implications for data minimization, focusing on the most influential frameworks. * Discuss the inherent challenges faced by general-purpose AI systems in implementing data minimization due to their extensive data needs and broad applications. * Define key terms and concepts related to PII minimization technologies. * Overview current and emerging technologies for minimizing PII in structured data, addressing their potential impacts and limitations. * Explore methods and challenges in minimizing PII in unstructured data. * Review data minimization in different industries and use cases.   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.
Autorenporträt
Patricia Thaine is the Co-Founder & CEO of Private AI, a Microsoft-backed startup who raised their Series A led by the BDC. Private AI was named a 2023 Technology Pioneer by the World Economic Forum and a Gartner Cool Vendor. Patricia was on Maclean's magazine Power List 2024 for being one of the top 100 Canadians shaping the country. She is also a Computer Science PhD Candidate at the University of Toronto (on leave) and a Vector Institute alumna. Her R&D work is focused on privacy-preserving natural language processing, with a focus on applied cryptography and re-identification risk. She also does research on computational methods for lost language decipherment. Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice "Trixie" Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She is the co-inventor of one U.S. patent and has ten years of research and software development experience, including at the McGill Language Development Lab, the University of Toronto's Computational Linguistics Lab, the University of Toronto's Department of Linguistics, and the Public Health Agency of Canada. Kathrin is a German- and Ontario-trained lawyer, working in privacy, data protection, and AI governance consulting. She is a Certified Information Privacy and AI Governance Professional, a philosophy PhD candidate at McMaster University, and a recipient of the SSHRC Graduate Scholarship. She has written extensively about regulatory issues and developments around privacy, data protection, and AI and led the privacy and data governance program at a Schedule I bank in Toronto. Kathrin is the Policy Lead of AI Governance and Safety Canada, an AI and Privacy Advisor at the venture capital firm Antler, and the founder of Responsible AI Consulting.