38 citations · 48 across the 4 of their papers we have counts for
4 papers
Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy
Mary Anne Smart, Priyanka Nanayakkara, Rachel Cummings +2
Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential…
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy
Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings +2
Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by…
Addressing Privacy Threats from Machine Learning
Mary Anne Smart
Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change,…
Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri +1
Deleting data from a trained machine learning (ML) model is a critical task in many applications. For example, we may want to remove the influence of training points that might be…