Search Results for author: Alex X. Liu

Found 10 papers, 3 papers with code

Label Inference Attacks Against Vertical Federated Learning

2 code implementations USENIX Security 22 2022 Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X. Liu, Ting Wang

However, we discover that the bottom model structure and the gradient update mechanism of VFL can be exploited by a malicious participant to gain the power to infer the privately owned labels.

Vertical Federated Learning

Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

no code implementations7 Apr 2022 Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang

To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

3 code implementations ICLR 2022 Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar

Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time.

Decision Making Management +2

Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning

no code implementations1 Jan 2021 Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.

Graph Classification Graph Matching +2

Deep Graph Matching and Searching for Semantic Code Retrieval

no code implementations24 Oct 2020 Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.

Graph Matching Retrieval

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

no code implementations20 Aug 2020 Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong

In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.

regression

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

1 code implementation8 Jul 2020 Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji

In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.

Graph Classification Graph Matching +4

Fine-grained Vibration Based Sensing Using a Smartphone

no code implementations8 Jul 2020 Kamran Ali, Alex X. Liu

VibroTag's accuracy is 37% higher than the average accuracy of 49. 25% achieved by one of the state-of-the-art IMUs based schemes, which we implemented for comparison with VibroTag.

Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging

no code implementations7 Jul 2020 Kamran Ali, Alex X. Liu, Eugene Chai, Karthik Sundaresan

The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging.

An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning

no code implementations19 Dec 2019 Zhiying Xu, Shuyu Shi, Alex X. Liu, Jun Zhao, Lin Chen

ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise.

Cannot find the paper you are looking for? You can Submit a new open access paper.