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07/06/2022
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to t...
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06/20/2022
Shuffle Gaussian Mechanism for Differential Privacy
We study Gaussian mechanism in the shuffle model of differential privacy...
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06/18/2022
Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning
Local differential privacy (LDP) gives a strong privacy guarantee to be ...
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06/07/2022
Shuffled Check-in: Privacy Amplification towards Practical Distributed Learning
Recent studies of distributed computation with formal privacy guarantees...
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04/08/2022
Network Shuffling: Privacy Amplification via Random Walks
Recently, it is shown that shuffling can amplify the central differentia...
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03/14/2022
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
How can we explore the unknown properties of high-dimensional sensitive ...
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06/08/2021
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
We propose a new framework of synthesizing data using deep generative mo...
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10/27/2020
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries
Transfer learning is a useful machine learning framework that allows one...
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06/22/2020
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
How can we release a massive volume of sensitive data while mitigating p...
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06/19/2020
Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
This paper studies how to learn variational autoencoders with a variety ...
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02/19/2020
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks
Graph convolutional neural networks, which learn aggregations over neigh...
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01/31/2020
Locally Private Distributed Reinforcement Learning
We study locally differentially private algorithms for reinforcement lea...
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11/20/2018
