Pii Minimization Handbook: Techniques, Challenges, and Solutions for Data Privacy Across Multiple Sectors
暫譯: Pii 最小化手冊：跨多個領域的數據隱私技術、挑戰與解決方案
Thaine, Patricia, Gardhouse, Kathrin
商品描述
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.
商品描述(中文翻譯)
這本書是一本全面且實用的指南，旨在最小化在金融、醫療保健、保險、法律、行銷、人力資源和政府等各種使用案例中可識別的個人資訊（PII）。
大多數資料保護法律和法規要求企業僅使用每個特定處理目的所需的最少 PII。在某些情況下，只有在資料完全匿名化的情況下，才允許進行處理。因此，PII 最小化描述了一個範圍，從幾乎不刪除任何直接識別符號到完全匿名化。
目前對於 PII 最小化的具體要求仍然不夠明確。PII 最小化的可行性和程度在很大程度上取決於要處理的資料集中的個人識別符號以及其處理的使用案例。
行業和使用案例特定的 PII 最小化標準提供了來自學術界以及將要涵蓋的七個行業的專家見解。這些專家澄清了在他們收集的資料集或其他可用資料中常見的個人識別符號，並指出在其行業中普遍存在的資料處理使用案例，以及每個使用案例中哪些個人識別符號是（不）必要的。
本書還介紹了正在開發技術解決方案以解決資料最小化難題的公司。這裡可以獲得的實用見解是如何在特定使用案例中以高準確度實現資料最小化，以滿足法規要求。例如，在開發面部識別軟體時，必須以機器可識別的形式使用人臉圖像。然而，當今的技術可以以這樣的方式修改面部圖像，使其對人類觀眾仍然可識別，但防止自動化系統的識別。
你將：
- 探索從基本資料減少策略到完全匿名化的 PII 最小化技術範圍。
- 檢視 AI 特定的法規及其對資料最小化的影響，重點關注最具影響力的框架。
- 討論通用 AI 系統在實施資料最小化時面臨的固有挑戰，因為它們對資料的需求廣泛且應用範圍廣泛。
- 定義與 PII 最小化技術相關的關鍵術語和概念。
- 概述當前和新興技術在結構化資料中最小化 PII 的情況，並探討其潛在影響和限制。
- 探索在非結構化資料中最小化 PII 的方法和挑戰。
- 回顧不同產業和使用案例中的資料最小化情況。
本書適合對象：
資料保護監管機構以及風險官、隱私和資料保護官、產品負責人、網路安全官、資訊官和在金融、醫療保健、保險、法律、行銷、人力資源和政府等領域內收集或處理 PII 的組織中的資料負責人，這些目的需要移除或模糊某些個人識別符號以滿足資料最小化要求。本書也適合為這七個行業制定可行的資料最小化標準的監管機構。
作者簡介
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.
作者簡介(中文翻譯)
Patricia Thaine 是 Private AI 的共同創辦人及董事長，這是一家由微軟支持的公司，其技術使組織能夠大規模檢測、理解、編輯和匿名化其數據。她的研發工作專注於保護隱私的自然語言處理、應用密碼學、重新識別風險，以及最大化數據在人工智慧（AI）和機器學習中的效用，這些都是個人識別資訊（PII）最小化的核心挑戰。
Patricia 被世界經濟論壇評選為 2023 年科技先驅，Private AI 被認可為 Gartner 在隱私領域的酷炫供應商，並在 2024 年的 PICCASO 獲得隱私創新獎。她是 Vector Institute 的校友，擁有美國專利的共同發明人，並擁有超過十年的研究和軟體開發經驗。Patricia 主持《The Data Frontier》播客，並在 2024 年被 Maclean's 評選為塑造國家的 100 位加拿大人之一。
Kathrin Gardhouse 是 Private AI 的前隱私宣導者。她目前轉向專注於歐盟和加拿大的 AI 政策研究。作為 The Future Society 的高級 AI 治理助理及 AI 治理與安全加拿大的政策負責人，她向政策制定者提供有關通用和代理 AI 風險及適當監管回應的建議。作為暑期研究學者，她為歐盟 AI 法案撰寫法律評論，並討論 AI 辦公室的執法權限。Kathrin 是受過法律訓練的律師，並擁有麥克馬斯特大學的哲學博士學位。她獲得 IAPP 認證，成為信息保護和 AI 治理專業人士。
