Xin Liu - IEEE Xplore Author Profile

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Author details

Xin Liu

Publications

3

Citations

58

Publications by Year

20222023

Co-Authors:

Sunder Ali KhowajaThong BachYoucheng BenQixuan CaiMinjie Cai

Show All Co-Authors (212)

Xin Liu

Affiliation

Faculty of Electrical Engineering and Computer Science

Ningbo University

Ningbo, China

Publication Topics

Adam Optimizer,
Attention Module,
Convolutional Layers,
Feature Maps,
L2 Loss,
Low-resolution Images,
Residual Block,
Super-resolution Network,
Artifact Reduction,
Attention Block,
Autoencoder,
Backward Propagation

Biography

Xin Liu received the B.S. degree in internet of things engineering in 2020 from the Ningbo University, Ningbo, China, where he is currently working toward the M.S. degree in communication engineering.,His research interests include deep learning for image processing and multisource image fusion.(Based on document published on 23 October 2023).

Publications

3

Citations

58

Publications by Year

20222023

Co-Authors:

Sunder Ali Khowaja
Thong Bach
Youcheng Ben
Qixuan Cai
Minjie Cai

Show All Co-Authors (212)

Author's Published Works

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Showing 1-4 of 4 results

Journals (2)

Conferences (1)

Early Access Articles (1)

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Weibin Wu(4)
Lu Zhou(3)
Zhe Liu(3)
Jun Wang(2)
Jipeng Zhang(1)
Zhejiang Laboratory, Zhejiang, China(2)
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-Belval Esch-sur-Alzette, Luxembourg(2)
State Key Laboratory of Cryptology, Beijing, China(1)
Zhejiang Lab, Hangzhou, China(1)
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), the Software School, Shandong University and Quan Cheng Laboratory, Jinan, China(1)
IEEE Transactions on Information Forensics and Security(2)
2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)(1)
IEEE Transactions on Services Computing(1)
IEEE(4)
Hong Kong(1)
Cloud Computing(2)
Execution Environment(2)
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Efficient and Privacy-Preserving Feature Selection based on Multiparty Computation

Luyao Wang;Hao Guo;Weibin Wu;Lu Zhou

IEEE Transactions on Information Forensics and Security

Year: 2025 | Early Access Article |

Feature selection is a critical data preprocessing stage that has been proven beneficial in data mining and machine learning applications. As most current works focus on privacy during the training and inference tasks in machine learning, implementing privacy preservation in preprocessing is a powerful complement. In this paper, we present an efficient and privacy-preserving feature selection prot...Show More

Efficient and Privacy-Preserving Feature Selection based on Multiparty Computation

Luyao Wang;Hao Guo;Weibin Wu;Lu Zhou

IEEE Transactions on Information Forensics and Security

Year: 2025 | Early Access Article |

ADSS: An Available-but-Invisible Data Service Scheme for Fine-Grained Usage Control

Hao Wang;Jun Wang;Chunpeng Ge;Yuhang Li;Lu Zhou;Zhe Liu;Weibin Wu;Mingsheng Cao

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

Cited by: Papers (1)

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The demand for mobile terminals to participate in data services is increasingly vital. The General Data Protection Regulation (GDPR) has established several principled requirements for data services. Existing studies focusing on data service put emphasis on data privacy and accessibility. However, they face challenges in achieving data forgetability and portability on mobile devices under GDPR and...Show More

ADSS: An Available-but-Invisible Data Service Scheme for Fine-Grained Usage Control

Hao Wang;Jun Wang;Chunpeng Ge;Yuhang Li;Lu Zhou;Zhe Liu;Weibin Wu;Mingsheng Cao

IEEE Transactions on Services Computing

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear Computations

Weibin Wu;Jun Wang;Yangpan Zhang;Zhe Liu;Lu Zhou;Xiaodong Lin

IEEE Transactions on Information Forensics and Security

Year: 2023 | Volume: 18 | Journal Article |

Cited by: Papers (2)

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The technique of packing multiple values into one message without losing homomorphic computation properties is the main workhorse that drives many exciting advances in applying lattice-based homomorphic encryption schemes to privacy-preserving Machine-Learning-as-a-Service (MLaaS). However, this technique does not directly work for the classic Paillier homomorphic encryption scheme, limiting the u...Show More

VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear Computations

Weibin Wu;Jun Wang;Yangpan Zhang;Zhe Liu;Lu Zhou;Xiaodong Lin

IEEE Transactions on Information Forensics and Security

Year: 2023 | Volume: 18 | Journal Article |

An Efficient and Scalable Sparse Polynomial Multiplication Accelerator for LAC on FPGA

Jipeng Zhang;Zhe Liu;Hao Yang;Junhao Huang;Weibin Wu

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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LAC, a Ring-LWE based scheme, has shortlisted for the second round evaluation of the National Institute of Standards and Technology Post-Quantum Cryptography (NIST-PQC) Standardization. FPGAs are widely used to design accelerators for cryptographic schemes, especially in resource-constrained scenarios, such as IoT. Sparse Polynomial Multiplication (SPM) is the most compute-intensive routine in LAC...Show More

An Efficient and Scalable Sparse Polynomial Multiplication Accelerator for LAC on FPGA

Jipeng Zhang;Zhe Liu;Hao Yang;Junhao Huang;Weibin Wu

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2020 | Conference Paper |

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