Boel Nelson
Associate senior lecturer/Assistant Professor at Department of Information Technology; Division of Computing Science
- E-mail:
- boel.nelson@it.uu.se
- Visiting address:
- Hus 10, Lägerhyddsvägen 1
- Postal address:
- Box 337
751 05 UPPSALA
- ORCID:
- 0000-0002-9746-4885
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Short presentation
I conduct research on privacy in a broad sense. My aim is to provide full stack approaches to privacy—putting theoretically sound principles into practice—with an end-goal of bringing meaningful privacy to people. I am interested in how information flows through computer systems, and if and how much information leaks during that process. In cases where data leaks I ask the question "can leakage be prevented?", and otherwise "can we quantify the leakage?".
Keywords
- anonymous communication
- censorship resilience
- data privacy
- differential privacy
- privacy enhancing technologies
- side-channels
- system security
Research
Right now I focus primarily on achieving provable privacy in instant messaging, under an umbrella of projects we call DenIM (deniable instant messaging). This work started with generous funding from MSCA in a project called Provable Privacy for Metadata (ProPriM).
Media
Metadata: the traces you didn't know you were leaving online
News article about the research project Provable Privacy for Metadata (ProPriM)
https://cs.au.dk/news-events/metadata-the-traces-you-didnt-know-you-were-leaving-online
Keynote on differential privacy
Differential Privacy: Principled Foundations, and Trade-offs in Applications, keynote at Open House at Mormor Karl's
https://www.youtube.com/watch?v=CpSjfmd_614
Anonymization—friend or foe?
Popular science presentation about anonymization given at MatchPoints 2024
https://www.youtube.com/watch?v=N9eREYOeW8A
Publications
Selection of publications
- PLAN: Variance-Aware Private Mean Estimation (2024)
- Differentially Private Selection from Secure Distributed Computing (2024)
- Metadata Privacy Beyond Tunneling for Instant Messaging (2024)
- Efficient Error Prediction for Differentially Private Algorithms (2021)
Recent publications
- PLAN: Variance-Aware Private Mean Estimation (2024)
- Differentially Private Selection from Secure Distributed Computing (2024)
- Metadata Privacy Beyond Tunneling for Instant Messaging (2024)
- Efficient Error Prediction for Differentially Private Algorithms (2021)
- SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication (2020)
All publications
Articles
Conferences
- PLAN: Variance-Aware Private Mean Estimation (2024)
- Differentially Private Selection from Secure Distributed Computing (2024)
- Metadata Privacy Beyond Tunneling for Instant Messaging (2024)
- Efficient Error Prediction for Differentially Private Algorithms (2021)
- Joint Subjective and Objective Data Capture and Analytics for Automotive Applications (2017)
- Introducing Differential Privacy to the Automotive Domain: Opportunities and Challenges (2017)
- Security and privacy for big data: A systematic literature review (2016)