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Cryptographers’ Track at the RSA Conference, CT-RSA 2025

作者： Ai, Jiayi Guo, Chun School of Cyber Science and Technology Shandong University Qingdao China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education Shandong University Shandong Qingdao266237 China Shandong Research Institute of Industrial Technology Shandong Jinan250102 China

ISBN: (纸本)9783031886607

We investigate the Misty structure with respect to the security notion of sequential indifferentiability of Mandal et al. (TCC 2012). As our main result, we prove sequential indifferentiability for 7-round Misty structures using seven independent random permutations in the rounds. We also prove insecurity for 5 rounds by exhibiting an attack. Our main result implies known-key security of 7 rounds in the sense of correlation intractability, providing the first known-key security results on Misty structures to our knowledge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Disentangled Noisy Correspondence Learning

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IEEE Transactions on Image Processing 2025年 34卷 2602-2615页

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong Xi’an Jiaotong University School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China SGIT AI Laboratory Xi’an710048 China State Grid Corporation of China State Grid Shaanxi Electric Power Company Ltd. Xi’an710048 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc. Beijing100085 China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of modality-invariant information (MII) and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal inputs for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL’s efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. © 1992-2012 IEEE.

关键词： information theory

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An efficient formal verification method for concurrent programs 4

An efficient formal verification method for concurrent progr...

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4th International Conference on Network Communication and information security, ICNCIS 2024

作者： Zhang, Jianyu Zhang, Long Zhang, Jingjing Yang, Feng University of Electronic Science and Technology of China 2006 Xiyuan Avenue Western District High-Tech Zone Chengdu611731 China National Key Laboratory of Science and Technology on Information System Security No. 13 Dachen Road Fengtai District Beijing100141 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Formal methods (FM) are a pivotal technology for ensuring the safety and reliability of software systems. These methods are centered around the application of precise mathematical principles to verify the security and dependability of software. Formal methods have been widely employed in the verification of critical systems, with the increasing significance of concurrent programs in the context of the growing prevalence of parallel computing architectures like multi-core processors. However, the efficiency of formal methods in verifying concurrent programs is often low, which hinders their broader adoption and application. Addressing this issue, this study proposes an efficient formal verification method tailored for concurrent programs. This method bifurcates the verification process of concurrent programs into two stages: proving sequential correctness and verifying other attributes. In the first stage, leveraging theorem-proving strategies and large language model (LLM) technology, the method assists in proving the sequential correctness of concurrent programs. The second stage, based on model checking technology, involves transforming the program to narrow down the verification scope before using model checking tools to verify other security attributes of the program. Experimental results have shown that the proposed method significantly reduces the state space that needs to be searched during verification, thereby enhancing the efficiency of concurrent program verification. © 2025 SPIE.

关键词： Model checking

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Validation of Sea Surface Winds from the Space-borne Radiometer COWVR

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12241-12247页

作者： Zhou, Luo Wang, Zhixiong Zhang, Naiqiang Qu, Jianhua State Key Laboratory of Climate System Prediction and Risk Management Nanjing210044 China Nanjing University of Information Science and Technology School of Marine Sciences Nanjing210044 China Beijng Huayun Shinetek Science and Technology Co. Ltd Beijing China

This study aims to validate sea surface wind data derived from the Compact Ocean Wind Vector Radiometer (COWVR) onboard the International Space Station (ISS). The COWVR, a fully polarimetric and two-look microwave radiometer, provides wind direction and speed retrievals in rain-free conditions. Validation was performed by comparing COWVR data with traditional radiometer (AMSR-2, GMI), scatterometer (MetOp\ASCAT, HY-2\SCAT), numerical weather prediction (ERA5), and buoy data. The results show that COWVR wind speed retrievals are comparable to those of AMSR-2 and GMI, with an overall wind speed bias close to zero and a standard deviation of 1.18 m\s when compared to ERA5. The COWVR also demonstrates good accuracy in wind direction retrievals for wind speeds above 8 m\s, with RMS errors of 12.5° and 15.1° for ERA5 and buoy comparisons, respectively. These findings suggest that COWVR can provide good sea surface wind products. However, the inconsistencies of radiometer and scatteromers sea surface wind speeds are still significant, especially for winds above 15 m\s. © 2008-2012 IEEE.

关键词： Radiometry

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年第11期12卷 16761-16776页

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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CRFENet: Cross-Spectral Stereo Matching Network Exploiting Reciprocal Feature Evolution 2nd

CRFENet: Cross-Spectral Stereo Matching Network Exploiting R...

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2nd International Conference on the Frontiers of Robotics and Software Engineering, FRSE 2024

作者： Li, Yangqin Zhang, Guanghui Zhu, Dongchen Shi, Wenjun Wang, Lei Zhang, Xiaolin Shanghai Institute of Microsystem and Information Technology Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9789819630127

Cross-spectral stereo matching opens up a new route for disparity estimation in low-light scenes. However, the variations in light absorption and reflection properties for objects at different wavelengths create substantial domain gaps in cross-spectral image pairs. Accurately matching pixel pairs with significant domain gaps remains challenging. In this work, we propose a novel lightweight cross-spectral stereo matching network, dubbed CRFENet, exploiting reciprocal feature evolution. It enables the complementary evolution of the disparity prediction network and spectral translation network. To facilitate feature interactions between different domains to improve disparity results in regions with significant domain gaps, we design a domain mutual rail (DMR) module, which integrates monocular spectral and binocular cross-spectral features. Furthermore, we present a domain gap consistency constraint and corresponding confidence-weighted loss function, which aim to alleviate the matching challenge in regions with significant domain gaps through decoupling domain gap and disparity. Experimental results indicate that our CRFENet achieves state-of-the-art performance on the PittsStereo RGB-NIR and DrivingStereo datasets, especially for challenging regions with significant domain gaps. Besides, the model parameters are only 3.6M, which means high practicality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereocenters

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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Tunable Synaptic Transistor With Volatile and Non-Volatile Switching Capabilities for Hierarchical Data Processing

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 789-792页

作者： Zhang, Guocheng Wang, Hongyu Qin, Shixian Tang, Jianchuan Zeng, Zili Su, Changqiang Yi, Xin Zhang, Xianghong Chen, Huipeng Fujian University of Technology Research Center for Microelectronics Technology Fuzhou350108 China Institute of Optoelectronic Display Fuzhou University Fuzhou350002 China Fudan University State Key Laboratory of Asic and System School of Microelectronics Shanghai200433 China Institute of Optoelectronic Display and Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou University Fuzhou350002 China

The hierarchical processing capabilities of the human visual system can significantly enhance the efficiency of data processing in the central nervous system. Volatile and non-volatile devices are key components in simulating the central nervous system. Realizing both volatile and non-volatile functionalities on a single device is ideal;however, challenges such as complex preparation and cumbersome switching persist. In this study, a tunable synaptic transistor with volatile and non-volatile switching capabilities is developed, offering ease of fabrication and convenient switching. It can simulate various forms of synaptic plasticity and exhibits excellent storage performance in non-volatile mode. Finally, we design an image preprocessing and classification system based on visual selective attention, which enables efficient neuromorphic computation through hierarchical data processing. © 1980-2012 IEEE.

关键词： Network security

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