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01/09/2023
Is Federated Learning a Practical PET Yet?
Federated learning (FL) is a framework for users to jointly train a mach...
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11/23/2022
Private Multi-Winner Voting for Machine Learning
Private multi-winner voting is the task of revealing k-hot binary vector...
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06/01/2022
On the reversibility of adversarial attacks
Adversarial attacks modify images with perturbations that change the pre...
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03/02/2022
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
Graph Neural Networks (GNNs) are powerful models designed for graph data...
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02/23/2022
Differentially Private Speaker Anonymization
Sharing real-world speech utterances is key to the training and deployme...
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02/06/2022
Pipe Overflow: Smashing Voice Authentication for Fun and Profit
Recent years have seen a surge of popularity of acoustics-enabled person...
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12/06/2021
When the Curious Abandon Honesty: Federated Learning Is Not Private
In federated learning (FL), data does not leave personal devices when th...
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11/17/2020
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Speaker identification models are vulnerable to carefully designed adver...
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08/13/2020
Semantically Adversarial Learnable Filters
We present the first adversarial framework that crafts perturbations tha...
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07/19/2020
Exploiting vulnerabilities of deep neural networks for privacy protection
Adversarial perturbations can be added to images to protect their conten...
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04/12/2020
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Exec...
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04/12/2020
PrivEdge: From Local to Distributed Private Training and Prediction
Machine Learning as a Service (MLaaS) operators provide model training a...
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11/25/2019
ColorFool: Semantic Adversarial Colorization
Adversarial attacks that generate small L_p-norm perturbations to mislea...
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10/27/2019
EdgeFool: An Adversarial Image Enhancement Filter
Adversarial examples are intentionally perturbed images that mislead cla...
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07/13/2019
Towards Characterizing and Limiting Information Exposure in DNN Layers
Pre-trained Deep Neural Network (DNN) models are increasingly used in sm...
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07/08/2019
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Recently, there has been a wealth of effort devoted to the design of sec...
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02/10/2018
Distributed One-class Learning
We propose a cloud-based filter trained to block third parties from uplo...
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02/09/2018
Deep Private-Feature Extraction
We present and evaluate Deep Private-Feature Extractor (DPFE), a deep mo...
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10/04/2017
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
Deep neural networks are increasingly being used in a variety of machine...
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03/08/2017
