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3rd International Conference on Blockchain Technology and information security, ICBCTIS 2023

作者： Qi, Hui Li, Hang Yu, Wangdong Wang, Chunbo Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology School of Information Technology Changchun Automobile Industry Institute Changchun China

ISBN: (纸本)9798350340402

With the quick development of Internet of Vehicles (IoV) technology, a lot of private data of vehicle users has been generated. An increasing amount of vehicle data is being collected in the cloud storage providers (CSP) and the edge node (EN). These data involve user privacy and vehicle driving data, and once they are damaged or lost, they will lead to serious traffic accidents. Therefore, safeguarding the security and integrity of IoV data has become a hot issue. However, due to the distributed nature of the IoV data system and the difficulty of edge node management, the traditional data integrity auditing methods can no longer satisfy the needs in the IoV cloud-edge collaboration scenario. Therefore, we propose a IoV edge data integrity audit method based on blockchain First, our design a novel cloud-edge collaborative storage model that not only allows both cloud and edge to participate in the audit, but also enables the data user (DU) to quickly access the IoV data and reduce the pressure of data transmission. Second, during the audit phase, we use the third-party auditor (TPA) to design a low-overhead audit algorithm and ensure that the privacy of the IoV data is not compromised. Finally, the TPA records audit transactions on the blockchain, so that the DU can re-review the TPA's audit results to find out whether the TPA is delaying and falsifying the audit result. Theoretical analysis shows that our method can protect the privacy of IoV data. Meanwhile, the experimental results show that our method has a low time overhead and the storage model is suitable for the IoV environment. © 2023 IEEE.

关键词： Blockchain

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A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

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computers, Materials & Continua 2024年第5期79卷 2085-2103页

作者： Jun Li Minqing Zhang Ke Niu Yingnan Zhang Xiaoyuan Yang College of Cryptography Engineering Engineering University of the Chinese People’s Armed Police ForceXi’an710086China Key Laboratory of Network and Information Security of the Chinese People’s Armed Police Force Xi’an710086China

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging *** the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in ***,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of *** then,we define the optimal rate of MVP as a steganalysis ***,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis *** experimental results demonstrate the effectiveness of the proposed feature ***,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

关键词： Video steganography video steganalysis motion vector prediction motion vector difference advanced motion vector prediction local optimality

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An Adaptive Image Compression Algorithm Based on Deep Learning 6

An Adaptive Image Compression Algorithm Based on Deep Learni...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Liang, Yanxia Zhao, Meng Lu, Guangyue Jiang, Jing He, Hua Jia, Tong Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In recent years, deep artificial neural networks have received wide attention because it surpasses the effect of traditional methods by parameter fitting under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity lead to necessity of image compression in communications. In this paper, we propose an adaptive image compression algorithm with variable compression rate and learning rate based on the neural network in deep learning. This algorithm can meet various requirements in different situations by adjusting the parameters reasonably. Experiment results show the feasibility, flexibility and effectiveness of our proposed algorithm. Lower errors are obtained compared to other algorithms under the same compression rate. © 2023 ACM.

关键词： Image compression

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Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing 5

Reversible Data Hiding Algorithm in Encrypted Domain Based o...

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5th International Conference on Electronics and Communication, network and computer Technology, ECNCT 2023

作者： Jiang, Zongbao Zhang, Minqing Ke, Yan Jiang, Chao University of PAP Key Laboratory of Network and Information Security of PAP Engineering Xi'an710086 China

ISBN: (纸本)9798350328660

Focused on the problems of user privacy disclosure and ciphertext data management in cloud service environment, this paper proposes a reversible data hiding algorithm in encrypted domain based on matrix secret sharing. Firstly, the matrix secret sharing of the pixels of the encrypted image was used to obtain n shares, in the process of constructing shared vectors for secret segmentation, the random variables involved in encryption and the location index of ciphertext feedback were used to construct the mapping relationship between them and the secret data to be embedded to generate redundant space for data embedding. Finally, the overflow pixels were processed, and the edge information was embedded by differential extension technology to obtain n marked encrypted images. The experimental results showed that the algorithm has complete reversibility. In contrast to the current algorithms, the embedding rate of the algorithm was greatly improved. The maximized embedding rate of the (3,4)-threshold scheme was 4.00bpp, and the maximized embedding rate of the (5,5)-threshold scheme was 6.40bpp, and the statistical characteristics such as correlation coefficient of the marked encrypted images were consistent with those of random image, and the security was high. The algorithm not only has the functions of privacy data protection and ciphertext authentication management, but also has the disaster recovery feature to realize the multi-party security storage of data. © 2023 IEEE.

关键词： Matrix algebra

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Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1984-1999页

作者： Feiye Zhang Qingyu Yang Dou An Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory for Manufacturing Systems Engineering(SKLMSE) Xi’an Jiaotong UniversityXi’an 710049 MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an 710049China

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

关键词： Centralized training and decentralized execution demand side management multi-agent reinforcement learning privacy preserving

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A Throughput and Priority Optimization Strategy for High Density Healthcare IoT

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Intelligent and Converged networks 2025年第1期6卷 41-52页

作者： Su, Zhenlang Ren, Junyu Huang, Yeheng Liao, Yang Qin, Tuanfa School of Computer Electronic and Information Guangxi University Nanning530004 China School of Physics and Electronic Information Guangxi Minzu University Nanning530006 China School of Computer Electronic and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China

In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4. © 2020 Tsinghua University Press.

关键词： Particle swarm optimization (PSO)

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PosFuzz:augmenting greybox fuzzing with effective position distribution

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Cybersecurity 2023年第4期6卷 123-143页

作者： Yanyan Zou Wei Zou JiaCheng Zhao Nanyu Zhong Yu Zhang Ji Shi Wei Huo Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Zhongguancun Laboratory BeijingChina

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and ***-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the ***,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing *** paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical *** makes use of a concept of effective position distribution to represent the semantics of the input and to guide the *** first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation *** then leverages two sampling methods in different mutating stages to select the positions from the *** have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M *** result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery *** with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on *** also triggers 275%more unique bugs on average.

关键词： Greybox fuzzing Mutation position Mutation operator Code coverage Vulnerability discovery

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Securing STAR-FC-RIS empowered integrated sensing and multiuser communications against target eavesdropping

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Science China(information Sciences) 2024年第11期67卷 335-346页

作者： Shuying LIN Yulong ZOU Fu XIAO Bin LI School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications

This paper investigates a simultaneous-transmitting-and-reflecting fully-connected reconfigurable intelligent surface(STAR-FC-RIS) empowered integrated sensing and multiuser communications(ISAMC) network, where a dual-functional radar-communication base station detects a malicious radar target nearby and communicates with multiple legitimate users on the other side of the STAR-RIS. We utilize an integrated architecture that combines the fully-connected(FC)-RIS, an emerging type of beyond-diagonal(BD)-RIS, with the time-switching(TS)-STAR-RIS to enhance both the sensing and communications at the cost of possible target intercepting and propose the simultaneous-transmitting-and-reflecting fully-connected RIS(STAR-FCR) schemes to strike a balance between sensing and communications performance. Thereafter, observing the security-reliability performance tradeoff of the downlink ISAMC, we conduct closed-form analyses to compare COPs of round-robin scheduling(RS) and multiuser scheduling(MS) with the aid of a TS-based STAR-FC-RIS. Furthermore, we derive closed-form expressions of the sensing outage probability, communications outage probability(COP), and communications intercept probability, where an average of the three probabilities is exploited to obtain an optimized time allocation(OTA) of the *** results verify that the STAR-FCR-MS scheme outperforms the STAR-FCR-RS scheme in terms of sensing reliability and communications security. Moreover, an OTA remarkably enhances the overall performance of the STAR-FCR schemes of ISAMC systems.

关键词： simultaneous-transmitting-and-reflecting reconfigurable intelligent surface (RIS) fully-connected beyond-diagonal RIS integrated sensing and communications multiuser scheduling security

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Tongda College of Nanjing University of Posts and Telecommunications Henan Key Laboratory of Network Cryptography Technology School of Computer Science and Technology Xidian University State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Faculty of Software Fujian Normal University

Ciphertext-Policy Attribute-Based Encryption(CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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Regularization Mixup Adversarial Training: A Defense Strategy for Membership Privacy with Model Availability Assurance 2

Regularization Mixup Adversarial Training: A Defense Strateg...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Ding, Zehua Tian, Youliang Wang, Guorong Xiong, Jinbo College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Neural network models face two highly destructive threats in real-world applications: membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the model's confidentiality, leading to membership privacy breaches, while the other disrupts the model's availability, rendering it dysfunctional. Recent work has shown that adversarial examples can pose dual threats to neural network models, both MIAs and AAs, so that these two different types of attacks have a certain degree of intersection. However, existing defense methods typically focus on defending against one of the two attacks and cannot simultaneously address both. To address the above issues, we propose a defense method called regularization mixup adversarial training (RMAT), aiming to protect model membership privacy while ensuring model availability. The core idea of RMAT is to use a continuous augmentation strategy during training to generate mixup adversarial examples, weakening attackers' understanding of membership data, and updating the model with a new membership-weighted loss strategy to improve its generalization ability for handling unknown samples. This approach can further protect the model's membership privacy and ensure model availability. Experimental results demonstrate that compared to single defense strategies, RMAT can simultaneously protect the model's membership privacy and availability. © 2024 IEEE.

关键词： Adversarial machine learning

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