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06/07/2022
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Federated learning (FL) has emerged as an effective approach to address ...
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02/15/2022
Defending against Reconstruction Attacks with Rényi Differential Privacy
Reconstruction attacks allow an adversary to regenerate data samples of ...
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09/25/2021
Opacus: User-Friendly Differential Privacy Library in PyTorch
We introduce Opacus, a free, open-source PyTorch library for training de...
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06/07/2021
Antipodes of Label Differential Privacy: PATE and ALIBI
We consider the privacy-preserving machine learning (ML) setting where t...
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03/01/2021
Wide Network Learning with Differential Privacy
Despite intense interest and considerable effort, the current generation...
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03/10/2020
Cryptanalytic Extraction of Neural Network Models
We argue that the machine learning problem of model extraction is actual...
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08/28/2019
Rényi Differential Privacy of the Sampled Gaussian Mechanism
The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and ...
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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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08/20/2018
Privacy Amplification by Iteration
Many commonly used learning algorithms work by iteratively updating an i...
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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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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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07/01/2016
