相關主題
商品描述
This book highlights the importance of digital privacy as an allied and supporting field to cybersecurity. The authors aim to underscore the fact that digital privacy is important sub-field of cybersecurity and must be differentiated from the social science and digital humanities view of privacy.
This book discusses digital privacy from various viewpoints in relation to cyber-security. The authors begin with Chapter 1, by emphasizing the fact that digital privacy must be viewed and addressed as a collective (and not an individual) problem. Therefore, solutions designed must include several perspectives ranging from decision making algorithms that assess the cost-benefit ratio for all parties involved in the digital operation. In Chapters 2, 3, 4 and 5, the authors discuss the implications from the adversarial and benign perspectives, of transforming data to ensure privacy. The authors also discuss performance, and some solutions to help alleviate this especially in scenarios involving large data and/or low powered/processing systems. In Chapters 6 and 7, the authors discuss the benefits of supporting user decision making and preventing privacy breaches that arise from inadvertent disclosures of sensitive personal information. Chapter 8 discusses possible avenues for future work centred around aspects, such as data transformation to support privacy preserving machine learning, privacy decision making and disclosure risks.
This book targets researchers working in digital privacy and cybersecurity as well as advanced-level students studying this field. Policy makers in governments and organizations will also find this book to be a valuable resource.
商品描述(中文翻譯)
這本書強調數位隱私作為網路安全的相關和支持領域的重要性。作者旨在強調數位隱私是網路安全的一個重要子領域，並且必須與社會科學和數位人文對隱私的看法區分開來。
本書從多個角度討論數位隱私與網路安全的關係。作者在第一章開始時強調，數位隱私必須被視為一個集體（而非個人）問題。因此，設計的解決方案必須包括多個視角，從決策算法開始，評估所有參與數位操作的各方的成本效益比。在第二、三、四和五章中，作者討論了從對抗性和良性視角轉換數據以確保隱私的影響。作者還討論了性能以及一些解決方案，以幫助減輕這一問題，特別是在涉及大量數據和/或低功耗/處理系統的情境中。在第六和第七章中，作者討論了支持用戶決策的好處，以及防止因無意洩露敏感個人信息而引發的隱私違規。第八章探討了未來工作的可能方向，集中於數據轉換以支持隱私保護的機器學習、隱私決策和洩露風險等方面。
本書的目標讀者是從事數位隱私和網路安全研究的研究人員，以及學習該領域的高級學生。政府和組織中的政策制定者也會發現這本書是個有價值的資源。
作者簡介
Anne Kayem is an Associate Professor in Cyber-Security and leads the Privacy AnaLytics (PAL) research Group at the University of Exeter. She holds a PhD in Computer Science obtained from Queen's University, Canada in 2009. She is an internationally recognised expert in the field of digital privacy focusing specifically on algorithms for data transformation to support privacy preserving machine learning and data analytics. She has written and edited several books about cyber-security and privacy notably on Access Control, Information Security, Secure micro-grids, and more recently on Digital Privacy. She is a senior member of the ACM and IEEE.
作者簡介(中文翻譯)
安妮·凱耶姆是埃克塞特大學的網路安全副教授，並領導隱私分析（Privacy AnaLytics, PAL）研究小組。她於2009年在加拿大女王大學獲得計算機科學博士學位。她是數位隱私領域的國際公認專家，專注於支持隱私保護的機器學習和數據分析的數據轉換算法。她撰寫和編輯了多本有關網路安全和隱私的書籍，特別是在存取控制、資訊安全、安全微電網以及最近的數位隱私方面。她是ACM和IEEE的資深會員。
