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 • 20 Oct 2022 • Guangsheng Zhang, Bo Liu, Huan Tian, Tianqing Zhu, Ming Ding, Wanlei Zhou
We investigate several representative model architectures from CNNs to Transformers, and show that Transformers are generally more vulnerable to privacy attacks than CNNs.
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.
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.
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 • 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 • 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.
2 code implementations • 23 Aug 2021 • Xinghao Yang, Weifeng Liu, James Bailey, Tianqing Zhu, DaCheng Tao, Wei Liu
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
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.
no code implementations • 12 Mar 2021 • Bo Liu, Ming Ding, Hanyu Xue, 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 • 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 • 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 • 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 • 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 • 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.
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.
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 • 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.