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07/16/2020
Data Poisoning Attacks Against Federated Learning Systems
Federated learning (FL) is an emerging paradigm for distributed training...
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07/11/2020
Understanding Object Detection Through An Adversarial Lens
Deep neural networks based object detection models have revolutionized c...
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06/05/2020
LDP-Fed: Federated Learning with Local Differential Privacy
This paper presents LDP-Fed, a novel federated learning system with a fo...
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04/22/2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Federated learning (FL) is an emerging distributed machine learning fram...
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04/09/2020
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
The rapid growth of real-time huge data capturing has pushed the deep le...
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01/25/2020
TiFL: A Tier-based Federated Learning System
Federated Learning (FL) enables learning a shared model across many clie...
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11/21/2019
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
Membership inference attacks seek to infer the membership of individual ...
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10/01/2019
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Deep neural network (DNN) has demonstrated its success in multiple domai...
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08/29/2019
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
Ensemble learning is a methodology that integrates multiple DNN learners...
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05/15/2019
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy
Local Differential Privacy (LDP) is popularly used in practice for priva...
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04/03/2019
Differentially Private Model Publishing for Deep Learning
Deep learning techniques based on neural networks have shown significant...
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12/07/2018
A Hybrid Approach to Privacy-Preserving Federated Learning
Training machine learning models often requires data from multiple parti...
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06/29/2018
Adversarial Examples in Deep Learning: Characterization and Divergence
The burgeoning success of deep learning has raised the security and priv...
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06/28/2018
