Isabel Wagner
2022–2024 Distinguished Visitor

Biography

Isabel Wagner is an Associate Professor in Computer Science (Cybersecurity) at De Montfort University in Leicester, UK. Dr. Wagner received her Ph.D in engineering (Dr.-Ing.) and M.Sc. in computer science (Dipl.-Inf. Univ.) from the Department of Computer Science, University of Erlangen in 2010 and 2005, respectively. She is a member of the editorial boards of Elsevier Ad Hoc Networks, Computer Networks, and Computer Communications. She also serves on the technical program committee of several top conferences (e.g., IEEE Infocom, IEEE LCN). She was recognized as a Senior Member of the ACM (2017) and IEEE (2018).

Her research interests are privacy and privacy-enhancing technologies, particularly metrics to quantify the effectiveness of privacy protection mechanisms, and privacy-enhancing technologies in smart cities, genomics, vehicular networks, and smart grids. She is also interested in bio-inspired mechanisms for privacy and web measurement to create transparency for corporate surveillance systems. Her new book “Auditing Corporate Surveillance Systems: Research Methods for Greater Transparency” will be published by Cambridge University Press in early 2022.

 

Email: iw@ieee.org

DVP term expires December 2024


Presentations

Big Tech’s Hunger for Data: the Case for Transparency – and Privacy-friendly Solutions

News about privacy invasions, discrimination, and biases in big tech platforms are commonplace today, and big tech’s reluctance to disclose how they operate counteracts ideals of transparency, openness, and accountability. This talk explains how big tech tracks users, how researchers study big tech systems to make them more transparent, and how privacy-friendly systems can be designed.

Machine Learning for Quantifying Web Privacy

In the past decade, researchers have used large-scale experimental measurements to quantify user privacy on the web and on mobile devices. Research questions focus on how and to what extent web/mobile usage causes user privacy loss, and to what extent countermeasures can reduce privacy loss. To answer these questions, researchers typically analyze application-layer network traffic to measure how user privacy can be compromised on the web. However, large-scale studies are difficult because manual analysis is too time-consuming and manually defined heuristics are often not good enough to correctly classify all cases.

This talk introduces machine learning and natural language processing and shows how they can be applied to automate classification tasks, scale up experiments, and enable large-scale quantification of web privacy.

Privacy-enhancing Technologies in Smart Cities

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on modern technologies to realize complex interactions between citizens, third parties, and city departments for a large number of application areas. This complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society.

This talk introduces privacy challenges in smart cities and explains how existing privacy-enhancing technologies – such as data anonymization, synthetic data generation, zero-knowledge-proofs, or anonymous credentials – can be applied to create privacy-friendly smart cities.

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Presentations

Big Tech’s Hunger for Data: the Case for Transparency – and Privacy-friendly Solutions

Machine Learning for Quantifying Web Privacy

Privacy-enhancing Technologies in Smart Cities

Read the abstracts for each of these presentations