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Luping Wang

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4 papers

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TCS Journal 2024 Journal Article

Efficient code-based fully dynamic group signature scheme

Luping Wang
Jie Chen
Huan Dai
Chongben Tao

Code-based group signature is an important research topic in recent years. Since the pioneering work by Alamélou et al. (WCC 2015), several other schemes have been proposed to provide improvements in security, efficiency and functionality. However, most existing constructions work only in the static setting where the group population is fixed at the setup phase. Only a few schemes address partially dynamic, which can realize only one of users enrollment or revocation. In this work, we provide an efficient code-based fully dynamic group signature (FDGS) scheme, i. e. , users have flexibility when joining and leaving the group. Specifically, to upgrade the scheme into a fully dynamic group signature, we first add a dynamic ingredient into the static 2-RNSD Merkle-tree accumulator (ASIACRYPT 2019), then create a simple rule and utilize the Stern-like zero-knowledge protocol to handle users enrollment and revocation efficiently (i. e. , without resetting the whole tree). Moreover, our solution is the first exploration of code-based FDGS with constant signature size.

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IS Journal 2022 Journal Article

Recognizing Slanted Deck Scenes by Non-Manhattan Spatial Right Angle Projection

Luping Wang
Hui Wei

Recognizing slanted deck scenes is crucial to security monitoring for protecting ships and making them behave smartly resilient. However, there are multitude of diverse structures that are designed as slanted planes due to rough maritime environments. Traditional methods for scene understanding from 3D point clouds or RGB-D data are energy-consuming and memory intensive, which makes those models less reliable in a resource-constrained system of limited compute, memory, and energy resources on ships. In this study, we present an approach to understanding deck scenes, including slanted structures, using a low-cost monocular camera without prior training. New clusters of slanted angle projections are extracted. The vanishing points of slanted non-Manhattan angle projections are estimated. These slanted planes can be reshaped by compositions of non-Manhattan angle projections. Combined with Manhattan planes, a deck scene can be approximated by Manhattan and non-Manhattan planes. Unlike deep learning-based algorithms, this approach requires no prior training or knowledge of the camera’s internal parameters. Experimental results demonstrated that the method can successfully elucidate diverse elements, including slanted structures, meeting safety monitoring requirements using a resource-constrained monocular camera in a deck environment.

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TCS Journal 2020 Journal Article

A post-quantum hybrid encryption based on QC-LDPC codes in the multi-user setting

Luping Wang
Jie Chen
Kai Zhang
Haifeng Qian

The encryption schemes based on coding theory are one of the most accredited choices in post-quantum scenario, where QC-LDPC codes are usually employed to construct concrete schemes due to the well security and good efficiency. In this work, we introduce a new IND-CCA secure multi-instance framework for code-based hybrid encryption primitive in the random oracle model, which is derived from our new multi-instance KEM and DEM building modules. We note that previous multi-instance KEM and DEM are usually derived from single-instance KEM and DEM, and hence suffers from large parameter sizes and security loss. Nevertheless, our multi-instance KEM is a direct construction based on a key generation function and a one-way trapdoor function, and our multi-instance DEM is constructed from a standard DEM and MAC with a tag in the input to achieve a tighter security loss. Finally, we present a IND-CCA secure multi-instance hybrid encryption scheme based on QC-LDPC codes in the random oracle model, where the scheme achieves small private key size and only consumes addition and multiplication operations over F 2 [ x ].

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EAAI Journal 2020 Journal Article

Understanding of wheelchair ramp scenes for disabled people with visual impairments

Luping Wang
Hui Wei

Helping disabled people with visual impairments understand wheelchair ramp scenes has considerable value in computer vision. However, due to the diversity of wheelchair ramp scenes, understanding them remains a big challenge. Wheelchair ramp planes can be considered as a composition of rectangles that do not satisfy manhattan assumption. These non-manhattan rectangles are projected into two dimensional projections, shaping into special geometric configurations, which may enable us to estimate their original orientation and position in 3D scenes. In this paper, we presented a method for disabled people with visual impairments to understand wheelchair ramp scenes from a single image without any prior training. Firstly angle projections can be assigned to different clusters. Secondly ramp vanishing points (RVPs) can be estimated. Then it is possible to determine ramp planes consisting of angle projections that belong to the estimated RVPs. Finally, the algorithm can understand wheelchair ramp scenes including not only manhattan structures but also ramp planes belonging to non-manhattan structures. The proposed approach requires no prior training or any knowledge of the camera’s internal parameters. Besides, it is robust to the errors in calibration and image noise. We compared the estimated wheelchair ramp scene layout against the ground truth, measuring the percentage of pixels that were incorrectly classified. The experimental results showed that the method can understand wheelchair ramp scenes including not only manhattan structures but also non-manhattan structures of ramp planes, making it practical and efficient for disabled people with visual impairments.

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