183 citations · 220 across the 10 of their papers we have counts for
10 papers
Boosting Model Inversion Attacks with Adversarial Examples
Shuai Zhou, Tianqing Zhu, Dayong Ye +2
Model inversion attacks involve reconstructing the training data of a target model, which raises serious privacy concerns for machine learning models. However, these attacks, espec…
New Challenges in Reinforcement Learning: A Survey of Security and Privacy
Yunjiao Lei, Dayong Ye, Sheng Shen +3
Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning h…
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy
Dayong Ye, Sheng Shen, Tianqing Zhu +2
Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to inf…
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
Dayong Ye, Huiqiang Chen, Shuai Zhou +3
Transfer learning is an important approach that produces pre-trained teacher models which can be used to quickly build specialized student models. However, recent research on trans…
Label-only Model Inversion Attack: The Attack that Requires the Least Information
Dayong Ye, Tianqing Zhu, Shuai Zhou +2
In a model inversion attack, an adversary attempts to reconstruct the data records, used to train a target model, using only the model's output. In launching a contemporary model i…
DP-Image: Differential Privacy for Image Data in Feature Space
Hanyu Xue, Bo Liu, Ming Ding +4
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even th…
Differential Advising in Multi-Agent Reinforcement Learning
Dayong Ye, Tianqing Zhu, Zishuo Cheng +2
Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an ad…
A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries
Dayong Ye, Tianqing Zhu, Shen Sheng +1
Cyber deception is one of the key approaches used to mislead attackers by hiding or providing inaccurate system information. There are two main factors limiting the real-world appl…
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Tianqing Zhu, Dayong Ye, Wei Wang +2
Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violation…
Differentially Private Multi-Agent Planning for Logistic-like Problems
Dayong Ye, Tianqing Zhu, Sheng Shen +2
Planning is one of the main approaches used to improve agents' working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private…