Boel Nelson
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Hi! :)

I'm Boel, and I'm passionate about learning. In fact, I like learning so much that I decided to work full-time with learning... which is why I'm a researcher!

When it comes to research, my favorite topic is privacy (in all forms and flavors). In particular, I have a soft spot for differential privacy. Generally, I like to explore holistic approaches to privacy. Speficially, I am interested in how information moves 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?". My research is focused around two main areas: data privacy and side-channels.

Currently, I hold two academic positions. First, I'm a tenure-track assistant professor in cybersecurity in the Computing Science Division at Uppsala University. Second, I'm a postdoc at Aarhus University working on my Marie Skłodowska-Curie Actions (MSCA) funded project Provable Privacy for Metadata (ProPriM). ProPriM is a two year project where I, together with Aslan Askarov, will explore formal methods in the context of achieving transport layer privacy.

Previously, I was a postdoc at University of Copenhagen (UCPH), in the Algorithms and Complexity section, and affiliated with Basic Algorithms Research Copenhagen (BARC). At UCPH I worked on Rasmus Pagh's project Providentia. Prior to joining UCPH, I was a postdoc in the Logic and Semantics group at Aarhus University, working on anonymous communication with Aslan Askarov. I earned my PhD (dissertation, topic: differential privacy) from Chalmers University of Technology. My PhD supervisor was David Sands.

Videos

Publications

  1. Martin Aumüller; Christian Janos Lebeda; Boel Nelson; Rasmus Pagh, "PLAN: Variance-Aware Private Mean Estimation", Proceedings on Privacy Enhancing Technologies, vol. 2024, no. 3, pp. 606–625, 2024, doi: 10.56553/popets-2024-0095.
  2. Boel Nelson; Elena Pagnin; Aslan Askarov, "Metadata Privacy Beyond Tunneling for Instant Messaging", In 2024 9th IEEE European Symposium on Security and Privacy (EuroS&P), July 9–11, 2024, Vienna, Austria, doi: 10.1109/EuroSP60621.2024.00044.
    • Runner-up for distinguished paper award
  3. Ivan Damgård; Hannah Keller; Boel Nelson; Claudio Orlandi; Rasmus Pagh, "Differentially Private Selection from Secure Distributed Computing", Proceedings of the ACM on Web Conference 2024 (WWW '24) . Association for Computing Machinery, New York, NY, USA, 1103–1114, doi: 10.1145/3589334.3645435.
  4. Boel Nelson, "Efficient Error Prediction for Differentially Private Algorithms", In The 16th International Conference on Availability, Reliability and Security (ARES 2021), August 17–20, 2021, Vienna, Austria. ACM, New York, NY, USA, 12 pages, doi: 10.1145/3465481.3465746.
  5. Boel Nelson; Jenni Reuben, "SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication", Transactions on Data Privacy 13:3 (2020) 201-245.
  6. Mathias Johanson; Jonas Jalminger; Emmanuel Frecon; Boel Nelson; Tomas Olovsson; Mats Gjertz, "Joint Subjective and Objective Data Capture and Analytics for Automotive Applications", 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, 2017, pp. 1-5, doi: 10.1109/VTCFall.2017.8288366
  7. Boel Nelson; Tomas Olovsson, "Introducing Differential Privacy to the Automotive Domain: Opportunities and Challenges", 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, 2017, pp. 1-7, doi: 10.1109/VTCFall.2017.8288389.
  8. Boel Nelson; Tomas Olovsson, "Security and Privacy for Big Data: A Systematic Literature Review", 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 3693-3702, doi: 10.1109/BigData.2016.7841037.

Preprints

  1. Boel Nelson; Aslan Askarov, "With a Little Help from My Friends: Transport Deniability for Instant Messaging", arxiv preprint, 2022
  2. Boel Nelson, "Randori: Local Differential Privacy for All", arxiv preprint, 2021

Posters

  1. Boel Nelson; Jenni Reuben, "Survey of Differentially Private Accuracy Improving Techniques for Publishing Histograms and Synthetic Data", at Open Day for Privacy, Usability, and Transparency (PUT 2019) held in conjunction with PETS'19
  2. Boel Nelson, "Randori: Differentially Private Data Collection Made Accessible", at EuroS&P'19