5 citations · 9 across the 3 of their papers we have counts for
4 papers
Improved Differentially Private Euclidean Distance Approximation
Nina Mesing Stausholm
This work shows how to privately and more accurately estimate Euclidean distance between pairs of vectors. Input vectors and are mapped to differentially private sketches $…
Infinitely Divisible Noise in the Low Privacy Regime
Rasmus Pagh, Nina Mesing Stausholm
Federated learning, in which training data is distributed among users and never shared, has emerged as a popular approach to privacy-preserving machine learning. Cryptographic tech…
Efficient Differentially Private Linear Sketching
Rasmus Pagh, Nina Mesing Stausholm
A powerful feature of linear sketches is that from sketches of two data vectors, one can compute the sketch of the difference between the vectors. This allows us to answer fine-gra…
Hardness of Bichromatic Closest Pair with Jaccard Similarity
Rasmus Pagh, Nina Stausholm, Mikkel Thorup
Consider collections and of red and blue sets, respectively. Bichromatic Closest Pair is the problem of finding a pair from $\mathcal{A}\times \mathcal{…