2 citations · 4 across the 9 of their papers we have counts for
9 papers
User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
Carla F. Griggio, Boel Nelson, Zefan Sramek +1
Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as password-protected chats and end-to-end encryption, which primarily protect mes…
Private Lossless Multiple Release
Joel Daniel Andersson, Lukas Retschmeier, Boel Nelson +1
Koufogiannis et al. (2016) showed a result for Laplace noise-based differentially private mechanisms: given an -DP release, a new release wi…
Differentially Private Selection from Secure Distributed Computing
Ivan Damgård, Hannah Keller, Boel Nelson +2
Given a collection of vectors , the selection problem asks to report the index of an "approximately largest" entry in .…
PLAN: Variance-Aware Private Mean Estimation
Martin Aumüller, Christian Janos Lebeda, Boel Nelson +1
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy…
Metadata Privacy Beyond Tunneling for Instant Messaging
Boel Nelson, Elena Pagnin, Aslan Askarov
Transport layer data leaks metadata unintentionally -- such as who communicates with whom. While tools for strong transport layer privacy exist, they have adoption obstacles, inclu…
With a Little Help from My Friends: Transport Deniability for Instant Messaging
Boel Nelson, Aslan Askarov
Traffic analysis for instant messaging (IM) applications continues to pose an important privacy challenge. In particular, transport-level data can leak unintentional information ab…
Efficient Error Prediction for Differentially Private Algorithms
Boel Nelson
Differential privacy is a strong mathematical notion of privacy. Still, a prominent challenge when using differential privacy in real data collection is understanding and counterac…
Randori: Local Differential Privacy for All
Boel Nelson
Polls are a common way of collecting data, including product reviews and feedback forms. However, few data collectors give upfront privacy guarantees. Additionally, when privacy gu…
SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication
Boel Nelson, Jenni Reuben
Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive…