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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Implementation of the OQDS Method for Principal Component Analysis 30th

Implementation of the OQDS Method for Principal Component...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer science, Computer Engineering and Applied Computing, CSCE 2024

作者： Chiyonobu, Miho Takata, Masami Kimura, Kinji Nakamura, Yoshimasa Graduate School of Humanities and Sciences Nara Women’s University Nara Japan Research Group of Information and Communication Technology for Life Nara Women’s University Nara Japan Faculty of Engineering University of Fukui Fukui Japan Faculty of Data Science Osaka Seikei University Osaka Japan

ISBN: (纸本)9783031856372

In this paper, we propose a method for computing partial singular values and the corresponding singular vectors. PCA (Principal Component Analysis) requires only larger singular values and the corresponding singular vectors. Generally, it is obtained by combining the bisection method and the inverse iteration method. However, there are some input matrices, such as the glued Kimura matrix, for which the inverse iteration method fails. Therefore, the OQDS (Orthogonal QD with Shift) method is adopted in this paper. The OQDS method can compute smaller singular values and the corresponding right singular vectors from a bi–diagonal matrix with high accuracy, in the case that the matrix is not split during the decomposition. However, usually, split occurs. Under split, it is not clear which side of the split the smaller singular values fall on. Therefore, to adopt the OQDS method to the PCA, it is necessary to consider how to deal with split. Thus, in this paper, we propose a new implementation of the OQDS method that is not effected by split. Experiments have confirmed that the method is fast while maintaining reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orthogonal QD with Shift method Principal Component Analysis Singular Value Decomposition

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Fast Computation Method for Stopping Condition of Range Restricted GMRES Method 30th

Fast Computation Method for Stopping Condition of Range Re...

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ISBN: (纸本)9783031856372

In this paper, we propose a method for fast computation of the stopping condition of the Range Restricted General Minimum Residual (RRGMRES) method. The RRGMRES method is iterative. As the number of iterations increases, the matrix size increases. The stopping condition for the iterations requires a condition number. The condition number is expressed as the ratio of the largest singular value to the smallest singular value. The proposed method employs the Cholesky LR method and inverse iteration. By comparing the experimental results with the conventional method, the proposed method has been 10 times faster than the conventional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Choquet integral

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Broad learning systems: An overview of recent advances, applications, challenges and future directions

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Neurocomputing 2025年 641卷

作者： Chu, Yonghe Guo, Yanlong Ding, Weiping Cao, Heling Ping, Peng School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Faculty of Data Science City University of Macau 999078 China School of Transportation and Civil Engineering Nantong University Jiangsu Nantong226001 China

Broad learning system (BLS) as a novel training framework derived from the random vector functional link neural network (RVFLNN). Unlike RVFLNN, which directly applies raw data to network learning, BLS first transforms input data into feature nodes through feature mapping. These feature nodes are then nonlinearly transformed into enhanced nodes. Both feature nodes and enhanced nodes are concatenated and connected to the output layer, and the corresponding output weights are derived via pseudo-inverse. Due to its shallow architecture and the need to train only the output weights, BLS achieves highly efficient learning ***, when adding new nodes, BLS does not require retraining from scratch;it only adjusts the weights associated with the new nodes. Different from deep learning, BLS aims to expand the broad rather than the depth of the neural network to solve complex problems. It not only overcomes the drawback of the long-training process in deep learning but also enables the rapid incremental construction of network models. Therefore, BLS has received extensive attention from both the academic and industrial communities. In view of this, this paper conducts a systematic review of BLS. First, we outline the research background of the BLS. Then, we elaborate on the relevant concepts and definitions of BLS. Furthermore, recent advances in BLS are introduced. In addition, we present the extensive applications of BLS in various fields. Finally, several possible development directions for BLS are proposed. © 2025 Elsevier B.V.

关键词： Optical character recognition

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Analyzing Students’ Emerging Roles Based on Quantity and Heterogeneity of Individual Contributions in Small Group Online Collaborative Learning Using Bipartite Network Analysis

arXiv

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arXiv 2025年

作者： Feng, Shihui Gibson, David Gašević, Dragan Faculty of Education University of Hong Kong Pok Fu Lam Road Hong Kong Institute of Data Science University of Hong Kong Pok Fu Lam Road Hong Kong Curtin University Perth Australia UNESCO France Faculty of Information Technology Monash University ClaytonVIC Australia

Understanding students’ emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning analytic methods based on student–subtask bipartite networks to gauge two conceptual dimensions—quantity and heterogeneity of individual contribution to subtasks—for understanding students’ emerging roles in online collaborative learning in small groups. We analyzed these two dimensions and explored the changes of individual emerging roles within seven groups of high school students (N = 21) in two consecutive collaborative learning projects. We found a significant association in the changes between assigned leadership roles and changes in the identified emerging roles between the two projects, echoing the importance of externally facilitated regulation scaffolding in CSCL. We also collected qualitative data through a semi-structured interview to further validate the quantitative analysis results, which revealed that student perceptions of their emerging roles were consistent with the quantitative analysis results. This study contributes new learning analytic methods for collaboration analytics as well as a two-dimensional theoretical framework for understanding students’ emerging roles in small group CSCL. © 2025, CC BY.

关键词： Collaborative learning

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A Near-Optimal Category information Sampling in RFID Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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A Study of Deep Learning Models for Identifying and Estimating Psychological Stress and Disorders Using Electroencephalogram Signals

A Study of Deep Learning Models for Identifying and Estimati...

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International Electrical Engineering Congress (iEECON)

作者： R. S. Lakshmi Balaji M. Batumalay Thaweesak Yingthawornsuk Avanthika Swamy Sirimonpak Suwannakhun Department of Advanced Computing Sciences Academy of Maritime Education and Training University (AMET) Chennai India Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Department of Media Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand Compliance Administrator Birmingham City Council United Kingdom Faculty of Industrial Education and Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The identification of psychological stress and disorders like panic attacks and anxiety is vital for improving mental health care. This study investigates the use of electroencephalogram (EEG) signals to predict these conditions, leveraging three deep learning models: a feedforward neural network, a transformer-based model, and a hybrid convolutional neural network (CNN)-long short-term memory network (LSTM). Performance metrics, including accuracy, F1 score, precision, recall, RMSE, and R-squared, were used for evaluation. The feedforward model achieved accuracies of 95.42% and 95.66% for predicting panic and anxiety attacks, respectively, while the transformer-based model also delivered promising results. Notably, the CNN-LSTM hybrid model outperformed the others, achieving 95.90% accuracy for anxiety prediction and excelling in stress level estimation with an RMSE of 0.1671 and an R-squared value of 0.9438. These findings demonstrate the hybrid model's efficacy in predicting psychological conditions and estimating stress levels. This approach holds significant promise for improving mental health assessments, early interventions, and treatment strategies.

关键词： Deep learning Accuracy Anxiety disorders Mental health Predictive models Brain modeling Transformers Electroencephalography Feedforward neural networks Convolutional neural networks

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Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market

arXiv

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arXiv 2025年

作者： Li, Songyuan Hu, Jia Min, Geyong Huang, Haojun Huang, Jiwei Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4PY United Kingdom School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China

The convergence of edge computing and Artificial Intelligence (AI) gives rise to Edge-AI, which enables the deployment of real-time AI applications and services at the network edge. One of the fundamental research issues in Edge-AI is edge inference acceleration, which aims to realize low-latency high-accuracy Deep Neural Network (DNN) inference services by leveraging the fine-grained offloading of partitioned inference tasks from end devices to edge servers. However, existing research has yet to adopt a practical Edge-AI market perspective, which would systematically explore the personalized inference needs of AI users (e.g., inference accuracy, latency, and task complexity), the revenue incentives for AI service providers that offer edge inference services, and multi-stakeholder governance within a market-oriented context. To bridge this gap, we propose an Auction-based Edge Inference Pricing Mechanism (AERIA) for revenue maximization to tackle the multi-dimensional optimization problem of DNN model partition, edge inference pricing, and resource allocation. We investigate the multi-exit device-edge synergistic inference scheme for on-demand DNN inference acceleration, and analyse the auction dynamics amongst the AI service providers, AI users and edge infrastructure provider. Owing to the strategic mechanism design via randomized consensus estimate and cost sharing techniques, the Edge-AI market attains several desirable properties, including competitiveness in revenue maximization, incentive compatibility, and envy-freeness, which are crucial to maintain the effectiveness, truthfulness, and fairness of our auction outcomes. The extensive simulation experiments based on four representative DNN inference workloads demonstrate that our AERIA mechanism significantly outperforms several state-of-the-art approaches in revenue maximization, demonstrating the efficacy of AERIA for on-demand DNN inference in the Edge-AI market. © 2025, CC BY.

关键词： Commerce

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Path optimization method for the sign problem caused by the fermion determinant

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Physical Review D 2025年第9期111卷 094503-094503页

作者： Kazuki Hisayoshi Kouji Kashiwa Yusuke Namekawa Hayato Takase Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation.

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