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Private Inference in Quantized Models

IEEE Transactions on Information Theory 2025年第5期71卷 3957-3973页

作者： Deng, Zirui Ramkumar, Vinayak Bitar, Rawad Raviv, Netanel Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv-Yafo6997801 Israel Institute for Communications Engineering Technical University of Munich Munich80333 Germany

A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. Private Inference has been extensively explored in recent years, mainly from a cryptographic standpoint via techniques like homomorphic encryption and multiparty computation. These approaches often come with high computational overhead and may degrade the accuracy of the model. In our work, we take a different approach inspired by the Private Information Retrieval literature. We view private inference as the task of retrieving inner products of parameter vectors with the data, a fundamental operation in many machine learning models. We introduce schemes that enable such retrieval of inner products for models with quantized (i.e., restricted to a finite set) weights;such models are extensively used in practice due to a wide range of benefits. In addition, our schemes uncover a fundamental tradeoff between user and server privacy. Our information-theoretic approach is applicable to a wide range of problems and robust in privacy guarantees for both the user and the server. © 1963-2012 IEEE.

关键词： Differential privacy

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Deterministic Learning-Based Tracking control for Parabolic PDE systems With Infinite-Dimensional Nonlinear Uncertain Dynamics

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IEEE Transactions on Automatic control 2025年第5期70卷 3272-3287页

作者： Zhang, Jingting Yuan, Chengzhi Wu, Fen Wang, Cong Cheng, Hong University of Electronic Science and Technology of China Center for Robotics School of Automation Engineering Chengdu611731 China University of Rhode Island Mechanical Industrial and Systems Engineering Department KingstonRI02881 United States North Carolina State University Mechanical and Aerospace Engineering Department RaleighNC27695 United States Shandong University School of Control Science and Engineering Jinan250061 China

In this article, we investigate the joint problem of dynamics learning and tracking control for a class of parabolic partial differential equation (PDE) systems with infinite-dimensional uncertain nonlinear dynamics. A new learning control scheme is proposed based on the deterministic learning (DL) theory. One key feature of the proposed scheme is its capability of accurately learning the system's nonlinear uncertain dynamics during real-time tracking control with provable stability and convergence of the overall PDE closed-loop system. Specifically, the Galerkin method is first employed to deal with the infinite dimensionality of the PDE system;a novel DL-based adaptive learning control scheme is then proposed using dual radial basis function neural networks (RBF NNs), in which a pair of RBF NNs are employed to address, respectively, the matched and unmatched components of uncertain nonlinear system dynamics. This control scheme is finally examined on the original PDE system, and it is rigorously proved that: first the PDE system's state tracks the prescribed reference trajectory with guaranteed closed-loop stability and tracking accuracy;and second locally accurate identification of the PDE system's dominant nonlinear uncertain dynamics can be achieved with provable convergence of associated NN weights to their optimal values, thereby the learned knowledge can be ultimately stored and represented by the convergent constant RBF NN models. Based on this, an experience-based control scheme is further proposed, which is capable of recalling the associated learned knowledge in real-time to further improve control performance and reduce computational complexity with maintained provable stabilization. It is worth stressing that although this work is focused particularly on parabolic PDE systems, it is groundbreaking with important technical breakthroughs that would facilitate a more complete extension of the DL theory from traditional ordinary differential equation syste

关键词： Galerkin methods

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Using Local Gradient Compensation in Federated Learning for Improving Privacy and Performance 14th

Using Local Gradient Compensation in Federated Learning for ...

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Jia-Wei Chen, Wei-Cheng Hung, Chih-Chieh Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789819623945

Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing that attackers can reconstruct private data from shared model parameters, compromising user privacy. To address this, various defense methods have been proposed, with gradient perturbation being a lightweight yet effective strategy. However, it often incurs a trade-off with model performance. In this paper, we introduce a lightweight defense mechanism tailored for Federated Stochastic Gradient Descent (FedSGD). Our method leverages maximum gradient values to gauge noise scale, while also preserving and compensating for previous noise iterations to mitigate performance impacts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Study on the frequencies of natural oscillations of car transporter wagon

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Noise and Vibration Worldwide 2025年第1-2期56卷 5-24页

作者： Muradian, Leontii Shvets, Anzhelika Nikitenko, Anatolii Shvets, Angela Department of Wagons and Wagon Facilities Ukrainian State University of Science and Technologies Dnipro Ukraine Division of Traction and Electrical Power Economics Electrical Power Engineering Institute Warsaw University of Technology Warsaw Poland Department of Engineering and Design Specialized Department Microprocessor-Based Control Systems and Safety in the Railway Transport Ukrainian State University of Science and Technologies Dnipro Ukraine

The development of new products and services allows railway transport to constantly increase the fleet of specialised wagons, which, in comparison with the universal rolling stock, have the best technical and economic indicators. A significant part of cars is delivered by railways, and it is economically reasonable to transport them on a specialised rolling stock. In this regard, the development of a methodology for refined theoretical studies of the strength of bodies of a double-deck rolling stock for passenger cars transportation, the choice of appropriate design schemes and parameters of the bodies, the analysis of the frequencies and forms of their natural oscillations, taking into account the finite stiffness of the elements, are urgent research tasks. The research uses the methods of structural mechanics of rod systems, the applied theory of elasticity, and discrete masses. A wide range of frequencies and forms of the natural free oscillations was obtained using the discrete mass method for a structural scheme with load transfer from the upper to the lower level of the wagon with the help of four racks. The applied methodology of theoretical determination of frequencies and forms of the natural free oscillations of the car transporter wagon by the method of discrete masses is effective. It allows for the estimation of the dynamic characteristics of the wagon. © The Author(s) 2024.

关键词： Bending oscillations twisting oscillations bouncing oscillations car transporter wagon double-deck rolling stock final rigidity of elements

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Quality-Relevant Modeling and Monitoring of Industrial Cyber-Physical systems: The Semi-Supervised Dynamic Latent Variable Models

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 39-47页

作者： Zhou, Le Wang, Yaoxin Wu, Yuanqing He, Shenghuang Song, Zhihuan Zhejiang University of Science and Technology Department of Automation and Electrical Engineering Hangzhou310023 China Guangdong University of Technology Department of Automation Guangzhou510006 China Dongguan University of Technology Department of Computer Science and Technology Dongguan523808 China Ningbo Industrial Internet Institute Ningbo315012 China Zhejiang University Department of Control Science and Engineering Hangzhou310027 China

In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complete process and quality variables with various sampling rates in the dynamic processes, a set of semi-supervised dynamic latent variable models are proposed for dynamic modeling and quality-relevant monitoring. The proposed models have built a unified structure to consider both the auto-correlations and cross-correlations between the process and quality variables with unbalanced sampling sizes. Hence, the feature extraction of the time series data is dynamically adjusted under the guidance of the quality variables. Then, the quality-relevant monitoring schemes are proposed, which is validated by a numerical case and an actual wastewater treatment process. © 2024 IEEE. All rights reserved.

关键词： Indexes Probabilistic logic Kalman filters Noise Cyber-physical systems Analytical models Parameter estimation Time series analysis Process monitoring Numerical models

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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Towards Securing Water systems Communications from Adversarial Attacks using AI Assurance 5

Towards Securing Water Systems Communications from Adversari...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Yardimci, Mehmet Oguz Batarseh, Feras A. Virginia Tech Department of Computer Science ArlingtonVA United States Virginia Tech Department of Biological Systems Engineering ArlingtonVA United States

ISBN: (纸本)9798331519957

Water systems are increasingly susceptible to cyberattacks due to their reliance on networked communications for monitoring and control. This paper introduces an AI-Assured approach to detect anomalies in water distribution systems communication layers using Long Short-Term Memory models, which are trained, tuned, and validated through four different scenarios targeting disruptions in communication patterns. Using the Secure Water Treatment and anomalies and malicious acts datasets, and by simulating both operational and malicious scenarios, Long Short-Term Memory model is trained on normal data and tested on both regular (idle) and attack situations. The LSTM model employs a sliding time-window technique to capture sequential dependencies in communication data;it identifies anomalies by comparing predicted and actual sequence-significant deviations that signal potential cyber threats. Our scenarios demonstrate an anomaly detection accuracy of 94%, with a false positive rate of 2.5%, indicating the model's capacity to identify both overt and subtle attacks. Further, we evaluate the intentionality of those outliers, whether they are cyber threats or merely a human operator error. The anomalies and malicious acts dataset scenario successfully detects multiple intentional sabotage scenarios, further validating the model's potential in real-world cyber-physical security settings. The findings suggest that integrating AI-Assured, LSTM-based anomaly detection into water systems can enhance security, enabling early threat detection and reducing the overall risk to critical infrastructures communications. © 2025 IEEE.

关键词： Artificial intelligence

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Generalized Two-Point Visual control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic systems and control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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