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Accurate and Efficient Privacy-Preserving Feature Extraction on Encrypted Images

IEEE Transactions on Dependable and Secure computing 2025年

作者： Zheng, Peijia Fang, Xiongjie Zhou, Zhipeng Yang, Rui Lu, Wei Cao, Xiaochun Huang, Jiwu Sun Yat-Sen University School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology MoE Key Laboratory of Information Technology Guangzhou510006 China Alibaba Group Hanzhou311121 China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518100 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China

In cloud computing, it is necessary to outsource image processing algorithms securely without exposing private image content. The scale-invariant feature transform (SIFT) is a famous local descriptor widely used in computer vision. There are already some privacy-preserving schemes for computing SIFT on encrypted images. However, the state-of-the-art works have to convert fixed-point numbers into their binary representations, which reduces efficiency and accuracy. In this paper, we propose a novel privacy-preserving SIFT scheme built from secure protocols designed explicitly for fixed-point numbers to solve this problem. Specifically, using RLWE-based homomorphic encryption, we propose word-wise protocols to perform secure division, square root operation, comparison, derivation, and matrix inversion in a single-instruction multiple-data manner. These protocols allow direct processing of fixed-point numbers without converting them to binary numbers, thus achieving high computational efficiency. We have also realized critical SIFT steps missing from previous works, including Euclidean gradient amplitude computation, histogram peak interpolation, and precise interval localization, leading to improved accuracy of SIFT features in the encrypted domain. We conduct security analysis and perform extensive experiments to evaluate the execution efficiency and accuracy. The experimental results show that the proposed scheme outperforms the state-of-the-art works in terms of computational efficiency and accuracy. © 2004-2012 IEEE.

关键词： Differential privacy

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A RIEMANNIAN EXPONENTIAL AUGMENTED LAGRANGIAN METHOD FOR computing THE PROJECTION ROBUST WASSERSTEIN DISTANCE

arXiv

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arXiv 2022年

作者： Jiang, Bo Liu, Ya-Feng Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projecting the distance measures onto a low-dimensional space is an efficient way of mitigating the curse of dimensionality in the classical Wasserstein distance using optimal transport. The obtained maximized distance is referred to as projection robust Wasserstein (PRW) distance. In this paper, we equivalently reformulate the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints. We propose a Riemannian exponential augmented Lagrangian method (ReALM) with a global convergence guarantee to solve this problem. Compared with the existing approaches, ReALM can potentially avoid too small penalty parameters. Moreover, we propose a framework of inexact Riemannian gradient descent methods to solve the subproblems in ReALM efficiently. In particular, by using the special structure of the subproblem, we give a practical algorithm named as the inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS). The remarkable features of iRBBS lie in that it performs a flexible number of Sinkhorn iterations to compute an inexact gradient with respect to the projection matrix of the problem and adopts the Barzilai-Borwein stepsize based on the inexact gradient information to improve the performance. We show that iRBBS can return an ǫ-stationary point of the original PRW distance problem within O(ǫ−3) iterations. Extensive numerical results on synthetic and real datasets demonstrate that our proposed ReALM as well as iRBBS outperform the state-of-the-art solvers for computing the PRW *** Codes 65K10, 90C26, 90C47 Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

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Adam-family methods for nonsmooth optimization with convergence guarantees

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 2398-2450页

作者： Nachuan Xiao Xiaoyin Hu Xin Liu Kim-Chuan Toh Institute of Operations Research and Analytics National University of Singapore Singapore School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks. We introduce a novel two-timescale framework that adopts a two-timescale updating scheme, and prove its convergence properties under mild assumptions. Our proposed framework encompasses various popular Adam-family methods, providing convergence guarantees for these methods in training nonsmooth neural networks. Furthermore, we develop stochastic subgradient methods that incorporate gradient clipping techniques for training nonsmooth neural networks with heavy-tailed noise. Through our framework, we show that our proposed methods converge even when the evaluation noises are only assumed to be integrable. Extensive numerical experiments demonstrate the high efficiency and robustness of our proposed methods.

关键词： nonsmooth optimization stochastic subgradient methods Adam nonconvex optimization gradient clipping

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Unconditionally optimal convergence of an energy-conserving and linearly implicit scheme for nonlinear wave equations

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Science China Mathematics 2022年第8期65卷 1731-1748页

作者： Waixiang Cao Dongfang Li Zhimin Zhang School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China Beijing Computational Science Research Center Beijing 100193China Department of Mathematics Wayne State UniversityDetroitMI 48202USA

In this paper,we present and analyze an energy-conserving and linearly implicit scheme for solving the nonlinear wave *** error estimates in time and superconvergent error estimates in space are established without certain time-step *** key is to estimate directly the solution bounds in the H-norm for both the nonlinear wave equation and the corresponding fully discrete scheme,while the previous investigations rely on the temporal-spatial error splitting *** examples are presented to confirm energy-conserving properties,unconditional convergence and optimal error estimates,respectively,of the proposed fully discrete schemes.

关键词： scalar auxiliary variable wave equations stability error estimate superconvergence

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Event generator for jet tomography in electron-ion collisions

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Physical Review D 2024年第3期110卷 034001-034001页

作者： Weiyao Ke Yuan-Yuan Zhang Hongxi Xing Xin-Nian Wang Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics (MOE) Theoretical Division Nuclear Science Division MS 70R0309 Physics Department School of Science and Engineering Hefei 230026 China Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center

We develop the first event generator, the electron-heavy-ion-jet-interaction-generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering undergo multiple collisions with the nuclear target. The collision rate is proportional to the transverse-momentum-dependent (TMD) gluon density in the nucleus, which is given by a simple model inspired by the physics of gluon saturation. Medium-modified QCD splitting functions within the higher-twist (HT) and generalized higher-twist (GHT) frameworks are utilized to simulate parton showering in the nuclear medium that takes into account the non-Abelian Landau-Pomeranchuck-Midgal interference effect. Employing eHIJING, we revisit hadron production in semi-inclusive deep inelastic scattering (SIDIS) as measured by EMC, HERMES, and recent CLAS experiments. eHIJING with both GT and GHT frameworks gives reasonably good descriptions of these experimental data. Predictions for experiments at the future electron-ion colliders are also provided. It is demonstrated that future measurements of the transverse momentum broadening of single hadron spectra can be used to map out the two-dimensional kinematic (Q2, xB) dependence of the jet transport coefficient q^ in cold nuclear matter.

关键词： Electron-ion collisions Fragmentation into hadrons QCD in nuclear reactions

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Continuous Geodesic Self-Attention Models with Gated Fusion for Trajectory Prediction

Continuous Geodesic Self-Attention Models with Gated Fusion ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kexin Ke Zhengyu Li Huining Chen Hao Wang Xian Wei Jian Yang Xuan Tang MoE Engineering Research MOE Center of SW/HW Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Advanced Technology Xi’an Jiaotong-Liverpool University China School of Geospatial Information Information Engineering University China School of Communications and Electronic Engineering East China Normal University China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Driven by the rapid development of intelligent driving vehicles, predicting the trajectories of pedestrians on the road is crucial for decision-making during driving and even road safety. In this paper, we propose a novel method for trajectory prediction, namely, Continuous Geodesic Self-Attention Models with Gated Fusion (CGSAG). We use geodesic attention to measure the similarity between trajectory points, and utilize a gating mechanism to fuse the geodesic features extracted by multi-layer graph convolution. We then use Neural Ordinary Differential Equations (Neural ODE) to model the continuous-time dynamics of the trajectory. We show that CGSAG improves state-of-the-art performances on several human trajectory prediction datasets, including ETH/UCY, SDD, and Ind. At the same time, we conduct ablation studies to prove the effectiveness and efficiency of our proposed method.

关键词： Pedestrians Logic gates Predictive models Feature extraction Mathematical models Time measurement Road safety

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EXPONENTIAL STABILITY FOR AN INFINITE MEMORY WAVE EQUATION WITH FRICTIONAL DAMPING AND LOGARITHMIC NONLINEAR TERMS

arXiv

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arXiv 2025年

作者： Peng, Qingqing Liu, Yikan School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China Department of Mathematics Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto606-8502 Japan

This article is concerned with the energy decay of an infinite memory wave equation with a logarithmic nonlinear term and a frictional damping term. The problem is formulated in a bounded domain in d (d ≥ 3) with a smooth boundary, on which we prescribe a mixed boundary condition of the Dirichlet and the acoustic types. We establish an exponential decay result for the energy with a general material density ρ(x) under certain assumptions on the involved coefficients. The proof is based on a contradiction argument, the multiplier method and some microlocal analysis techniques. In addition, if ρ(x) takes a special form, our result even holds without the damping effect, that is, the infinite memory effect alone is strong enough to guarantee the exponential stability of the *** Codes 35L20, 35L70, 35B40 Copyright © 2025, The Authors. All rights reserved.

关键词： Damping

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ENERGY DECAY OF NONLOCAL VISCOELASTIC EQUATIONS WITH NONLINEAR DAMPING AND POLYNOMIAL NONLINEARITY

arXiv

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arXiv 2025年

This paper is concerned with the energy decay of a viscoelastic variable coefficient wave equation with nonlocality in time as well as nonlinear damping and polynomial nonlinear terms. Using the Lyapunov method, we establish a polynomial energy decay for the solution under relatively weak assumptions regarding the kernel of the nonlocal term. More specifically, we improve the decay rate of the energy by additionally imposing a certain convexity assumption on the kernel. Several examples are provided to confirm such improvements to faster polynomial or even exponential *** Codes 35L20, 35L70, 35B40 Copyright © 2025, The Authors. All rights reserved.

关键词： Damping

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Macroscopic finite-difference scheme based on the mesoscopic regularized lattice-Boltzmann method

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Physical Review E 2024年第2期109卷 025301-025301页

作者： Xi Liu Ying Chen Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China

In this paper, we develop a macroscopic finite-difference scheme from the mesoscopic regularized lattice Boltzmann (RLB) method to solve the Navier-Stokes equations (NSEs) and convection-diffusion equation (CDE). Unlike the commonly used RLB method based on the evolution of a set of distribution functions, this macroscopic finite-difference scheme is constructed based on the hydrodynamic variables of NSEs (density, momentum, and strain rate tensor) or macroscopic variables of CDE (concentration and flux), and thus shares low memory requirement and high computational efficiency. Based on an accuracy analysis, it is shown that, the same as the mesoscopic RLB method, the macroscopic finite-difference scheme also has a second-order accuracy in space. In addition, we would like to point out that compared with the RLB method and its equivalent macroscopic numerical scheme, the present macroscopic finite-difference scheme is much simpler and more efficient since it is only a two-level system with macroscopic variables. Finally, we perform some simulations of several benchmark problems, and find that the numerical results are not only in agreement with analytical solutions, but also consistent with the theoretical analysis.

关键词： Complex fluids Lattice-Boltzmann methods Navier-Stokes equation

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Blockchain-Assisted Privacy-Preserving and Synchronized key Agreement for VDTNs

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Wang, Chenhao Ming, Yang Liu, Hang Feng, Jie Yang, Mengmeng Xiang, Yang Chang'an University School of Information Engineering Xi'an710064 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Commonwealth Scientific and Industrial Research Organization Australia Swinburne University of Technology Department of Computing Technologies VIC3122 Australia

With the continuous development of digitization evolutions, vehicular digital twin networks (VDTNs) facilitate traffic data and optimization results to be exchanged between the vehicle and digital twin as well as shared among a group of digital twins. However, the data exchange and group sharing processes take place in real-time over public communication channels, which suffer from various security and privacy threats. key agreement technologies are promising to enable secure data communications for entities, but the existing key agreement schemes generally fail to fulfill the requirements of synchronization, privacy, and entity management for VDTNs. Therefore, we propose a blockchain-assisted privacy-preserving and synchronized key agreement scheme for VDTNs. In the proposed scheme, the anonymous vehicle and digital twin can negotiate a secret session key in the case of synchronization to achieve secure data exchange. Meanwhile, digital twins are capable of utilizing synchronized state information to dynamically establish a common group encryption key but hold individual decryption keys, which guarantee the security of group sharing. Additionally, the proposed scheme is able to protect identity privacy and manage vehicles and digital twins with the assistance of blockchain and smart contract. The security analysis demonstrates that the proposed scheme provides security and privacy assurances for VDTNs. The performance evaluation indicates that it has excellent expressions in terms of efficiency, practicality, and smart contract consumption. © 2004-2012 IEEE.

关键词： Differential privacy

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