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pro vyhledávání: '"Nelson, Boel"'
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 and utility is well understood in the worst case, many datasets exhibi
Externí odkaz:
http://arxiv.org/abs/2306.08745
Given a collection of vectors $x^{(1)},\dots,x^{(n)} \in \{0,1\}^d$, the selection problem asks to report the index of an "approximately largest" entry in $x=\sum_{j=1}^n x^{(j)}$. Selection abstracts a host of problems--in machine learning it can be
Externí odkaz:
http://arxiv.org/abs/2306.04564
Transport layer data leaks metadata unintentionally -- such as who communicates with whom. While tools for strong transport layer privacy exist, they have adoption obstacles, including performance overheads incompatible with mobile devices. We posit
Externí odkaz:
http://arxiv.org/abs/2210.12776
Autor:
Nelson, Boel, Askarov, Aslan
Traffic analysis for instant messaging (IM) applications continues to pose an important privacy challenge. In particular, transport-level data can leak unintentional information about IM -- such as who communicates with whom. Existing tools for metad
Externí odkaz:
http://arxiv.org/abs/2202.02043
Autor:
Nelson, Boel
Differential privacy is a strong mathematical notion of privacy. Still, a prominent challenge when using differential privacy in real data collection is understanding and counteracting the accuracy loss that differential privacy imposes. As such, the
Externí odkaz:
http://arxiv.org/abs/2103.04816
Autor:
Nelson, Boel
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 guarantees are given upfront, they are often vague claims about 'anonymi
Externí odkaz:
http://arxiv.org/abs/2101.11502
SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication
Autor:
Nelson, Boel, Reuben, Jenni
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 attributes, can result in privacy leakage. To enable data analysis, a
Externí odkaz:
http://arxiv.org/abs/1910.14028
Transport layer data leaks metadata unintentionally$\unicode{x2013}$such as who communicates with whom. While tools for strong transport layer privacy exist, they have adoption obstacles, including performance overheads incompatible with mobile devic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0deca6a9d373e76adb9318985c5a3f5