2.3k citations · 8.3k across the 36 of their papers we have counts for
40 papers
Auditing Private Prediction
Karan Chadha, Matthew Jagielski, Nicolas Papernot +2
Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound. Auditing t…
The Adversarial Implications of Variable-Time Inference
Dudi Biton, Aditi Misra, Efrat Levy +6
Machine learning (ML) models are known to be vulnerable to a number of attacks that target the integrity of their predictions or the privacy of their training data. To carry out th…
Training Private Models That Know What They Don't Know
Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta +2
Training reliable deep learning models which avoid making overconfident but incorrect predictions is a longstanding challenge. This challenge is further exacerbated when learning h…
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search
Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot +1
Models need to be trained with privacy-preserving learning algorithms to prevent leakage of possibly sensitive information contained in their training data. However, canonical algo…
Measuring Forgetting of Memorized Training Examples
Matthew Jagielski, Om Thakkar, Florian Tramèr +8
Machine learning models exhibit two seemingly contradictory phenomena: training data memorization, and various forms of forgetting. In memorization, models overfit specific trainin…
The Privacy Onion Effect: Memorization is Relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang +3
Machine learning models trained on private datasets have been shown to leak their private data. While recent work has found that the average data point is rarely leaked, the outlie…