no code implementations • 5 Aug 2020 • Tianqing Zhu, Dayong Ye, Wei Wang, Wanlei Zhou, Philip S. Yu
Artificial Intelligence (AI) has attracted a great deal of attention in recent years.
no code implementations • 16 Aug 2020 • Dayong Ye, Tianqing Zhu, Sheng Shen, Wanlei Zhou, Philip S. Yu
To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.
no code implementations • 14 Sep 2020 • Tao Zhang, Tianqing Zhu, Mengde Han, Jing Li, Wanlei Zhou, Philip S. Yu
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
no code implementations • 25 Sep 2020 • Tao Zhang, Tianqing Zhu, Jing Li, Mengde Han, Wanlei Zhou, Philip S. Yu
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
no code implementations • 7 Oct 2020 • Tao Zhang, Tianqing Zhu, Ping Xiong, Huan Huo, Zahir Tari, Wanlei Zhou
In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed.
no code implementations • 9 Aug 2020 • Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam
Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years.
Cryptography and Security
no code implementations • 19 Oct 2020 • Sheng Shen, Tianqing Zhu, Di wu, Wei Wang, Wanlei Zhou
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server.
Distributed, Parallel, and Cluster Computing
no code implementations • 12 Mar 2021 • Hanyu Xue, Bo Liu, Ming Ding, Tianqing Zhu, Dayong Ye, Li Song, Wanlei Zhou
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.
no code implementations • 13 Mar 2022 • Dayong Ye, Sheng Shen, Tianqing Zhu, Bo Liu, Wanlei Zhou
The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
no code implementations • 13 Mar 2022 • Dayong Ye, Huiqiang Chen, Shuai Zhou, Tianqing Zhu, Wanlei Zhou, Shouling Ji
However, they may not mean that transfer learning models are impervious to model inversion attacks.
no code implementations • 13 Mar 2022 • Dayong Ye, Tianqing Zhu, Shuai Zhou, Bo Liu, Wanlei Zhou
In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i. e., black-box attacks, or the parameters of a target model, i. e., white-box attacks.
no code implementations • 22 Mar 2022 • Chi Liu, Huajie Chen, Tianqing Zhu, Jun Zhang, Wanlei Zhou
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.
no code implementations • 28 Sep 2022 • Mengde Han, Tianqing Zhu, Wanlei Zhou
The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed.
no code implementations • 20 Oct 2022 • Guangsheng Zhang, Bo Liu, Huan Tian, Tianqing Zhu, Ming Ding, Wanlei Zhou
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.
no code implementations • 31 Dec 2022 • Yunjiao Lei, Dayong Ye, Sheng Shen, Yulei Sui, Tianqing Zhu, Wanlei Zhou
A large number of studies have focused on these security and privacy problems in reinforcement learning.
no code implementations • 23 Mar 2023 • Huajie Chen, Tianqing Zhu, Yuan Zhao, Bo Liu, Xin Yu, Wanlei Zhou
By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.
no code implementations • 2 Jun 2023 • Chi Liu, Tianqing Zhu, Sheng Shen, Wanlei Zhou
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes.
no code implementations • 6 Jun 2023 • Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.
no code implementations • 25 Jun 2023 • Huiqiang Chen, Tianqing Zhu, Tao Zhang, Wanlei Zhou, Philip S. Yu
Federated learning (FL) has been a hot topic in recent years.
no code implementations • 24 Jun 2023 • Shuai Zhou, Tianqing Zhu, Dayong Ye, Xin Yu, Wanlei Zhou
Hence, in this paper, we propose a new training paradigm for a learning-based model inversion attack that can achieve higher attack accuracy in a black-box setting.
no code implementations • 19 Aug 2023 • Hui Sun, Tianqing Zhu, Wenhan Chang, Wanlei Zhou
Based on the substitution mechanism and fake label, we propose a cascaded unlearning approach for both item and class unlearning within GAN models, in which the unlearning and learning processes run in a cascaded manner.
no code implementations • 9 Oct 2023 • Hu Zhang, Xin Shen, Heming Du, Huiqiang Chen, Chen Liu, Hongwei Sheng, Qingzheng Xu, MD Wahiduzzaman Khan, Qingtao Yu, Tianqing Zhu, Scott Chapman, Zi Huang, Xin Yu
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions.
no code implementations • 7 Nov 2023 • Huan Tian, Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou
It leverages the difference in the predictions from both the original and fairness-enhanced models and exploits the observed prediction gaps as attack clues.
no code implementations • 26 Dec 2023 • Dayong Ye, Tianqing Zhu, Congcong Zhu, Derui Wang, Zewei Shi, Sheng Shen, Wanlei Zhou, Minhui Xue
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners.
no code implementations • 19 Feb 2024 • Jiyao Li, Mingze Ni, Yifei Dong, Tianqing Zhu, Wei Liu
At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied.
no code implementations • 23 Mar 2024 • Youyang Qu, Ming Ding, Nan Sun, Kanchana Thilakarathna, Tianqing Zhu, Dusit Niyato
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
1 code implementation • 21 Apr 2024 • Huiqiang Chen, Tianqing Zhu, Xin Yu, Wanlei Zhou
Current research centres on efficient unlearning to erase the influence of data from the model and neglects the subsequent impacts on the remaining data.
1 code implementation • 19 May 2021 • Yuexin Xiang, Tiantian Li, Wei Ren, Tianqing Zhu, Kim-Kwang Raymond Choo
Experimental findings on the testing set show that our scheme preserves image privacy while maintaining the availability of the training set in the deep learning models.
1 code implementation • 10 Apr 2022 • Yuexin Xiang, Yuchen Lei, Ding Bao, Wei Ren, Tiantian Li, Qingqing Yang, Wenmao Liu, Tianqing Zhu, Kim-Kwang Raymond Choo
Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications.
2 code implementations • 14 Aug 2020 • Yuexin Xiang, Tiantian Li, Wei Ren, Tianqing Zhu, Kim-Kwang Raymond Choo
We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples.