Enabling Privacy Preserving Data Analytics

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.

Distinguishes the field of Digital Privacy from Digital (cyber) security Highlights the interdependencies between good digital privacy and security assurances Covers domains such as attacks to data pseudonymisation and anonymisation schemes

Autorentext

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.

Inhalt

Part I Overview.- Chapter 1 Introduction.- Part II Data De-Anonymisation.- Chapter 2 De-Anonymisation Mechanisms - An Overview.- Part III Anonymisation Approaches,- Chapter 3 Multi-Objective Anonymisation.- Chapter 4 High-Dimensional Data - Privacy Considerations.- Chapter 5 Accounting for User Privacy Preferences.- Part IV Usable Privacy - A Discussion.- Chapter 6 Privacy Recommender Systems.- Chapter 7 Identifying Personal Information in Textual Data.- Part V Conclusions and Future Work.- Chapter 8 Conclusions.- Appendix 1.- Appendix 2.- Glossary.- Index.