7 citations · 7 across the 11 of their papers we have counts for
3 papers · 1 filter
LLM-as-a-Discriminator: When Synthetic Tables Still Look Real
Manel Slokom, Malek Slokom, Thierno Kante
Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular data, auditing privacy remains…
When Machine Learning Models Leak: An Exploration of Synthetic Training Data
Manel Slokom, Peter-Paul de Wolf, Martha Larson
We investigate an attack on a machine learning model that predicts whether a person or household will relocate in the next two years, i.e., a propensity-to-move classifier. The att…
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation
Manel Slokom, Özlem Özgöbek, Martha Larson
This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a…