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Threshold Password-Hardening Updatable Oblivious Key Management

IEEE Transactions on Information Forensics and security 2025年 20卷 4799-4814页

作者： Jiang, Changsong Xu, Chunxiang Liu, Zhen Dong, Xinfeng Zhang, Wenzheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore Huzhou313001 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Science and Technology on Communication Security Laboratory Chengdu610041 China

We propose a threshold password-hardening updatable oblivious key management system dubbed TPH-UOKM for cloud storage. In TPH-UOKM, a group of key servers share a user-specific secret key for a user, and assist the user in producing her/his password-derived private key in a threshold and oblivious way, where the password is hardened to resist offline dictionary guessing attacks. Anyone can outsource data protected with the user's password-derived public key to the cloud server, and merely the user holding the correct password can recover the password-derived private key for data access. TPH-UOKM can accomplish decryption of N ciphertexts with the complexity O(1) of communication between a user and the key servers, which outperforms existing schemes. TPH-UOKM supports password update. The cloud server can update all protected data of a user with an update token to be accessible only with the new password, which resists password leakage. We present a two-level proactivization mechanism to periodically update user-specific secret key shares and the key servers to thwart perpetual compromise of them, where the renewal of user-specific secret key shares reduces computation and communication costs compared to existing approaches. Provable security and high efficiency of TPH-UOKM are demonstrated by comprehensive analyses and performance evaluations. © 2005-2012 IEEE.

关键词： Information management

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Few-Sample Video Captioning Using Pre-Trained Language Model with Gated Bidirectional Fusion

SSRN

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SSRN 2025年

作者： Wang, Tao Li, Ping Pan, Zeyu Wang, Hao School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Security Hangzhou Dianzi University Hangzhou China

Video captioning generates sentences for describing the video content. Previous works mostly rely on a large number of video samples for training the model, but annotating those videos is very costly, limiting the widespread application of video captioning. This motivates us to explore the video captioning when using only a few labeled samples, and propose a few-sample video captioning method by adopting the Pre-trained language model with Gated Bidirectional Fusion (PGBF). In particular, we design a triple dynamic gating module that dynamically adjusts the contributions of appearance, motion, and text information to balance the visual information from videos and the linguistic knowledge from pre-trained language models. Also, we apply a bidirectional fusion module to fuse appearance-text features and motion-text features in a more effective way. Moreover, we introduce a semantic contrastive loss to minimize the gap between the corresponding visual features (i.e., appearance feature, motion feature, and their fusion one) and text features (i.e., extracted from the parsed nouns, verbs and whole sentence). Extensive experiments on three popular benchmarks demonstrate that our method achieves promising video captioning performance by using only a few training samples. © 2025, The Authors. All rights reserved.

关键词： Semantics

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FeedbackFuzz: Fuzzing Processors via Intricate Program Generation with Feedback Engine

FeedbackFuzz: Fuzzing Processors via Intricate Program Gener...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiashun Wang Baojiang Cui Renhai Dong Rundi Zhai School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As modern processor designs become increasingly complex, detecting hardware vulnerabilities has become more challenge. Recently, hardware fuzzing techniques have shown promising results in generating complex programs for processor testing. However, the complexity of processors continues to limit the speed of vulnerability detection and the ability to achieve sufficient *** paper introduces FeedbackFuzz, a novel processor fuzzer aimed at significantly accelerating vulnerability detection. FeedbackFuzz enhances detection efficiency by generating more effective test programs and optimizing the process of identifying vulnerabilities in longer programs. It designs a feedback engine for processors with out-of-order execution capabilities. Additionally, we leverage large language models (LLMs) to handle the complexity of locating vulnerabilities in lengthy programs, greatly speeding up the detection process. We evaluated FeedbackFuzz on several open-source RISC-V processors. Our evaluation demonstrates that the efficiency of FeedbackFuzz has increased by 40% compared to Cascade, with a coverage acceleration of 16.7%.

关键词： Out of order Location awareness Fuzzing Signal processing Hardware Complexity theory security Speech processing Engines Testing

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Performance Limits of Shadowing Effects-Based Passive Target Localization Assisted by Active Sensor Calibration via Visible Light Positioning

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IEEE Transactions on Communications 2025年

作者： Bao, Xu Zhou, Jiawei Zhang, Licheng The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China The Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China

In this paper, we aim at exploring the performance limits of passive indoor target localization utilizing multiple light-emitting diode (LED) transmitters embedded into the ceiling and multiple photodiode (PD) sensors anchored on the floor, where the precise locations of the PDs are not predetermined. This work enriches our understanding of error evolution in the time-domain localization cooperation. We propose a novel solution to estimate the target position by identifying the PDs occluded by the target, through measuring the changes in received signal strength (RSS) caused by the shadowing effects induced by this indoor target. Concurrently, these LEDs are used for the positional calibration of the PDs involved in the localization process. Specifically, we derive the Cramér-Rao lower bound (CRLB) on the estimation errors for both target localization and sensor position calibration, and then conduct the convergence and asymptotic performance analyses based on the obtained error bounds. Finally, simulation results corroborate the performance analysis, elucidating how the localization error evolves until it converges, and identifying the factors that influence this intrinsic convergence mechanism. The derived CRLBs and the long-term localization performance from these bounds provide a theoretical basis for optimizing passive positioning algorithms without requiring any auxiliary receiver. © 1972-2012 IEEE. All rights reserved.

关键词： Cramer-Rao bounds

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Enhancing Scalability: A Complete Tree Sharding Architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. This paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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DPCZK: Enhancing Device Privacy Through Certificate-Free Encryption and Zero Knowledge Proof in Multi-Domain IoT Environments

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IEEE Internet of Things Journal 2025年

作者： Ma, Hongmei Liu, Yifan Leu, Yi Feng, Fan Liu, Zhenpeng Hebei University School of Cyberspace Security and Computer Baoding071000 China Hebei University School of Management Baoding071000 China Hebei University Information Technology Center Baoding071000 China

The vast number of IoT devices is distributed across multiple trust domains, each with distinct security policies, trust models, and permission management methods. This diversity increases the risk of privacy exposure during cross-domain communications. At the same time, traditional authentication methods have problems such as complex certificate management, high risk of key escrow, and reliance on trusted third parties. To address the above problems, this paper proposes a novel method, enhancing device privacy through certificateless encryption and zero-knowledge proof (DPCZK). DPCZK achieves decentralization by leveraging a consortium blockchain as a trust bridge across different domains. The adoption of certificateless encryption mitigates the incomplete trust issues associated with the key generation center. Furthermore, DPCZK incorporates an identity-hiding mechanism based on zero-knowledge proof, enabling devices to authenticate and interact with resources anonymously during cross-domain operations, thereby safeguarding their privacy. Additionally, through threshold technology, the target domain can reveal the true identities of malicious devices and revoke their access rights, ensuring a balanced approach to security and privacy protection. The proposed scheme has been experimentally validated in a virtual environment and compared with existing solutions. Results demonstrate that DPCZK offers significant improvements in both effectiveness and efficiency. © 2014 IEEE.

关键词： Differential privacy

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A Verifiable Decentralized Data Modification Mechanism Supporting Accountability for Securing Industrial IoT 24th

A Verifiable Decentralized Data Modification Mechanism Suppo...

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

作者： Yang, Changsong Wu, Junfu Liang, Hai Ding, Yong Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (纸本)9789819615247

The widespread application of Industrial Internet of Things (IIoT) has profoundly transformed modern industrial production methods. However, the large scale of industrial data, the diversity of data sources, and the heterogeneity of data structures make data maintenance exceptionally challenging. The editability techniques have been introduced into blockchain to leverage its distributed and editable features for data maintenance and management. However, existing solutions mainly focus on achieving editability while overlooking data management personnel’s privacy protection and lacking effective modification verification mechanisms to ensure data reliability. To address these issues, this paper proposes a verifiable decentralized data modification mechanism supporting accountability (DDMA) for securing IIoT. Our DDMA mechanism not only achieves the redactability of blockchain but also protects the privacy of data management personnel and provides reliable clues for subsequent accountability. Additionally, the DDMA mechanism offers an efficient method for modification verification, enabling rapid validation of whether on-chain data has been modified. Theoretical analysis demonstrates that DDMA meets the requirements of modification authorization, behavior traceability, and efficient modification verification. Experimental results indicate that DDMA can reduce modification and verification overheads. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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New MRA Schemes Based on The CRT for Polynomial Rings

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IEEE Transactions on Information Theory 2025年第6期71卷 4671-4685页

作者： Yang, Jing Wang, Xianfang Xiang, Can Fu, Fang-Wei Xia, Shu-Tao Shandong Technology and Business University School of Computer Science and Technology Shandong Yantai264005 China CETC Cyberspace Security Technology Co. Ltd Westone Cryptologic Research Center Beijing100070 China South China Agricultural University College of Mathematics and Informatics Guangdong Guangzhou510642 China Nankai University Chern Institute of Mathematics LPMC Tianjin300071 China Tsinghua University Tsinghua Shenzhen International Graduate School Guangdong Shenzhen518055 China

At present, existing multi-receiver authentication (MRA) schemes can only handle situations where the capacities of all receivers in the schemes are the same. However, in reality, different receivers may need to have different storage capacities. In this paper, inspired by the secret sharing scheme based on the Chinese Remainder Theorem (CRT) for polynomial rings where each participant holds the share with different sizes, we propose three new constructions of unconditionally secure MRA schemes for multiple messages using the CRT for polynomial rings, including a (k, n)-threshold MRA scheme, a (k, n, w)-weighted threshold MRA scheme, and a (Q, F)-general MRA scheme. As far as we know, our proposed MRA schemes are the first MRA schemes with different storage capacities for different receivers and the first ones based on the CRT for polynomial rings. Moreover, the proposed schemes can be seen as extensions of the MRA scheme in Safavi-Naini and Wang (Eurocrypt’98, LNCS, 1403, 527-541, 1998). In particular, as for our (Q, F)-general MRA scheme, it has generally more communication complexity and much less computation complexity than the existing (Q, F)- general MRA scheme. © 1963-2012 IEEE.

关键词： Authentication

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M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment

arXiv

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arXiv 2025年

作者： Cui, Chuan Chen, Kejiang Wei, Zhihua Shen, Wen Zhang, Weiming Yu, Nenghai School of Computer Science and Technology Tongji University Shanghai China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China

The rapid advancement of AI-generated image (AGI) models has introduced significant challenges in evaluating their quality, which requires considering multiple dimensions such as perceptual quality, prompt correspondence, and authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive framework for AGI quality assessment that is Multimodal, Multi-Round, and Multi-Aspect. Our approach leverages the capabilities of Multimodal Large Language Models (MLLMs) as joint text and image encoders and distills advanced captioning capabilities from online MLLMs into a local model via Low-Rank Adaptation (LoRA) fine-tuning. The framework includes a structured multi-round evaluation mechanism, where intermediate image descriptions are generated to provide deeper insights into the quality, correspondence, and authenticity aspects. To align predictions with human perceptual judgments, a predictor constructed by an xLSTM and a regression head is incorporated to process sequential logits and predict Mean Opinion Scores (MOSs). Extensive experiments conducted on multiple benchmark datasets demonstrate that M3-AGIQA achieves state-of-the-art performance, effectively capturing nuanced aspects of AGI quality. Furthermore, cross-dataset validation confirms its strong generalizability. The code is available at https://github. com/strawhatboy/M3-AGIQA. Copyright © 2025, The Authors. All rights reserved.

关键词： Image quality

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A Novel Vulnerability-Detection Method Based on the Semantic Features of Source Code and the LLVM Intermediate Representation

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Journal of Software: Evolution and Process 2025年第5期37卷

作者： Chen, Jinfu Zhou, Jiapeng Lin, Wei Towey, Dave Cai, Saihua Chen, Haibo Chen, Jingyi Yin, Yemin School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China School of Computer Science University of Nottingham Ningbo China Ningbo China

With the increasingly frequent attacks on software systems, software security is an issue that must be addressed. Within software security, automated detection of software vulnerabilities is an important subject. Most existing vulnerability detectors rely on the features of a single code type (e.g., source code or intermediate representation [IR]), which may lead to both the global features of the code slices and the memory operation information not being captured or considered. In particular, vulnerability detection based on source-code features cannot usually include some macro or type definition content. In this paper, we propose a vulnerability-detection method that combines the semantic features of source code and the low level virtual machine (LLVM) IR. Our proposed approach starts by slicing (C/C++) source files using improved slicing techniques to cover more comprehensive code information. It then extracts semantic information from the LLVM IR based on the executable source code. This can enrich the features fed to the artificial neural network (ANN) model for learning. We conducted an experimental evaluation using a publicly-available dataset of 11,381 C/C++ programs. The experimental results show the vulnerability-detection accuracy of our proposed method to reach over 96% for code slices generated according to four different slicing criteria. This outperforms most other compared detection methods. © 2025 John Wiley & Sons Ltd.

关键词： C (programming language)

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