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12/14/2020
Extracting Training Data from Large Language Models
It has become common to publish large (billion parameter) language model...
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10/29/2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
We develop techniques to quantify the degree to which a given (training ...
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08/08/2019
That which we call private
A casual reader of the study by Jayaraman and Evans in USENIX Security 2...
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11/29/2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Sensitive statistics are often collected across sets of users, with repe...
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02/24/2018
Scalable Private Learning with PATE
The rapid adoption of machine learning has increased concerns about the ...
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02/22/2018
The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
Machine learning models based on neural networks and deep learning are b...
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11/30/2017
TCAV: Relative concept importance testing with Linear Concept Activation Vectors
Neural networks commonly offer high utility but remain difficult to inte...
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08/26/2017
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense int...
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10/18/2016