3 papers
cs.CR2026
PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
Wan Ki Wong, Sahel Torkamani, Michele Ciampi +1
Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often un…
cs.LG2026
Magnitude Distance: A Geometric Measure of Dataset Similarity
Sahel Torkamani, Henry Gouk, Rik Sarkar
Quantifying the distance between datasets is a fundamental question in mathematics and machine learning. We propose \textit{magnitude distance}, a novel distance metric defined on…
cs.CR2024
Count on Your Elders: Laplace vs Gaussian Noise
Joel Daniel Andersson, Rasmus Pagh, Teresa Anna Steiner +1
In recent years, Gaussian noise has become a popular tool in differentially private algorithms, often replacing Laplace noise which dominated the early literature. Gaussian noise i…