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cs.LG2025
Hot PATE: Private Aggregation of Distributions for Diverse Task
Edith Cohen, Benjamin Cohen-Wang, Xin Lyu +3
The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptati…
cs.LG2025
The Cost of Compression: Tight Quadratic Black-Box Attacks on Sketches for Norm Estimation
Sara Ahmadian, Edith Cohen, Uri Stemmer
Dimensionality reduction via linear sketching is a powerful and widely used technique, but it is known to be vulnerable to adversarial inputs. We study the black-box adversarial se…