183 citations · 710 across the 28 of their papers we have counts for
29 papers
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…
Momentum Gradient Descent Federated Learning with Local Differential Privacy
Mengde Han, Tianqing Zhu, Wanlei Zhou
Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to…
How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
Guangsheng Zhang, Bo Liu, Huan Tian +3
As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns…
BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis
Yuexin Xiang, Yuchen Lei, Ding Bao +6
Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to…
Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack
Chi Liu, Huajie Chen, Tianqing Zhu +2
DeepFakes are raising significant social concerns. Although various DeepFake detectors have been developed as forensic countermeasures, these detectors are still vulnerable to atta…
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…
Electric Field Induced Macroscopic Cellular Phase of Nanoparticles
Abigail Rendos, Wenhan Cao, Margaret Chern +5
A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase under the collective influence of AC and DC voltages. Systematic…
Semantic-Preserving Adversarial Text Attacks
Xinghao Yang, Weifeng Liu, James Bailey +2
Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods…
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning
Yuexin Xiang, Tiantian Li, Wei Ren +2
To ensure the privacy of sensitive data used in the training of deep learning models, a number of privacy-preserving methods have been designed by the research community. However,…
Fair and Differentially Private Distributed Frequency Estimation
Mengmeng Yang, Ivan Tjuawinata, Kwok-Yan Lam +2
In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical to…
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…
Improving Utility of Differentially Private Mechanisms through Cryptography-based Technologies: a Survey
Wen Huang, Shijie Zhou, Tianqing Zhu +1
Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of…
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security
Sheng Shen, Tianqing Zhu, Di Wu +2
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes…
Correlated Differential Privacy: Feature Selection in Machine Learning
Tao Zhang, Tianqing Zhu, Ping Xiong +3
Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differe…
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
Tao Zhang, Tianqing Zhu, Jing Li +3
A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learn…
Fairness Constraints in Semi-supervised Learning
Tao Zhang, Tianqing Zhu, Mengde Han +3
Fairness in machine learning has received considerable attention. However, most studies on fair learning focus on either supervised learning or unsupervised learning. Very few cons…
Generating Image Adversarial Examples by Embedding Digital Watermarks
Yuexin Xiang, Tiantian Li, Wei Ren +2
With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific wa…
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…
Local Differential Privacy and Its Applications: A Comprehensive Survey
Mengmeng Yang, Lingjuan Lyu, Jun Zhao +2
With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of us…
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…
Privacy Intelligence: A Survey on Image Privacy in Online Social Networks
Chi Liu, Tianqing Zhu, Jun Zhang +1
Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent…