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05/25/2023
Learning across Data Owners with Joint Differential Privacy
In this paper, we study the setting in which data owners train machine l...
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05/24/2023
Privacy Implications of Retrieval-Based Language Models
Retrieval-based language models (LMs) have demonstrated improved interpr...
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04/21/2023
Matching-based Data Valuation for Generative Model
Data valuation is critical in machine learning, as it helps enhance mode...
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02/21/2023
kNN-Adapter: Efficient Domain Adaptation for Black-Box Language Models
Fine-tuning a language model on a new domain is standard practice for do...
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05/17/2022
Recovering Private Text in Federated Learning of Language Models
Federated learning allows distributed users to collaboratively train a m...
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11/30/2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Gradient inversion attack (or input recovery from gradient) is an emergi...
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09/08/2021
EMA: Auditing Data Removal from Trained Models
Data auditing is a process to verify whether certain data have been remo...
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12/23/2020
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
Missing value imputation is a challenging and well-researched topic in d...
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10/22/2020
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
To address the issue that deep neural networks (DNNs) are vulnerable to ...
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10/12/2020
TextHide: Tackling Data Privacy in Language Understanding Tasks
An unsolved challenge in distributed or federated learning is to effecti...
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10/06/2020
InstaHide: Instance-hiding Schemes for Private Distributed Learning
How can multiple distributed entities collaboratively train a shared dee...
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05/22/2020
Deep Learning Based Detection and Localization of Cerebal Aneurysms in Computed Tomography Angiography
Detecting cerebral aneurysms is an important clinical task of brain comp...
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03/04/2020
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network prunin...
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04/19/2019
