Beyond Object Authentication: Context-Closed Post-Quantum Authentication for the WebPKI
本文解决了WebPKI在后量子迁移中认证成本增加及授权上下文不一致的问题，提出了一种名为LRp的双平面后量子结构来验证可变CA上下文状态。
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Selected work
Representative Papers
TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation
This work addresses the lack of trustworthy, stage-wise evaluation in existing Graph-RAG systems, which hinders precise error localization. The authors propose TRIAGE, a novel framework that introduces the first phase-aware evaluation mechanism spanning the entire Graph-RAG pipeline. TRIAGE establishes an interpretable diagnostic chain through a three-stage metric suite: KG construction (assessing triple confidence, source coverage, and schema consistency), KG validation (measuring structural quality, correctness, and completeness), and KG utilization (evaluating retrieval coverage, faithfulness, and query cost). This enables accurate mapping of failures to specific, tunable components. The framework supports both online unsupervised diagnosis in gold-label-free settings and offline supervised calibration when ground truth is available, and is accompanied by a formal theoretical foundation, a proof-of-concept implementation, and a reproducible evaluation protocol.
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I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
Existing methods struggle to disentangle aliased degradation mechanisms in multi-sensor systems and fail to quantify uncertainty in health indicators (HIs). To address this, we propose an input-grouping-driven HI construction framework. First, we introduce projection-path reconstruction into HI generation—integrated with Monte Carlo Dropout and probabilistic latent space modeling—to jointly quantify perceptual (data-driven) and cognitive (model-driven) uncertainties. Second, we propose a sensor-subset grouping paradigm that enables mechanism-specific degradation modeling by leveraging domain knowledge. Evaluated on aerospace and manufacturing system datasets, our method significantly improves remaining useful life (RUL) prediction accuracy and cross-domain generalizability. Moreover, it provides interpretable insights into failure evolution pathways through uncertainty-aware HI trajectories and grouped sensor contributions.
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Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation
Under energy transition, high renewable penetration and cross-border interconnections intensify grid uncertainty, rendering conventional power flow solvers inadequate for real-time operation, while purely data-driven models lack physical consistency. This paper proposes a physics-informed neural network (PINN) framework for power flow simulation, integrating Kirchhoff’s laws and other physical priors via hybrid modeling—combining MLP/GNN architectures with physics-constrained regularization and unsupervised loss. We introduce a novel four-dimensional evaluation framework (accuracy, physical consistency, industrial deployability, out-of-distribution generalization) and the LIPS benchmark platform. Systematic ablation studies demonstrate that explicit graph-structured modeling and direct optimization of physical equations are critical to reliability. The resulting model achieves high accuracy, strong physical consistency, robust out-of-distribution generalization, and practical industrial deployment potential. Code is fully open-sourced.
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AUTOPSY: A Framework for Tackling Privacy Challenges in the Automotive Industry
Intelligent connected vehicles (ICVs) face urgent practical challenges in enhancing privacy-friendliness beyond mere GDPR compliance. Method: This study proposes the first privacy engineering framework tailored for full-vehicle systems, integrating system modeling, a dynamic privacy manager, a GDPR principle–guided PETs selection methodology, and a layered privacy architecture to enable fine-grained data-flow control and automated compliance mapping. The modular design decouples privacy functionality for scalable deployment. A prototype is implemented and validated in a location-based service scenario. Contribution/Results: The framework ensures end-to-end controllability over user data collection, transmission, and processing; achieves privacy-policy response latency under 200 ms; and improves PETs configuration coverage by 40%. It establishes a technically advanced, regulation-adaptive privacy enhancement paradigm for automotive systems.
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Latest Papers
Beyond Object Authentication: Context-Closed Post-Quantum Authentication for the WebPKI
本文解决了WebPKI在后量子迁移中认证成本增加及授权上下文不一致的问题，提出了一种名为LRp的双平面后量子结构来验证可变CA上下文状态。
0 citationsRead paper
TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation
This work addresses the lack of trustworthy, stage-wise evaluation in existing Graph-RAG systems, which hinders precise error localization. The authors propose TRIAGE, a novel framework that introduces the first phase-aware evaluation mechanism spanning the entire Graph-RAG pipeline. TRIAGE establishes an interpretable diagnostic chain through a three-stage metric suite: KG construction (assessing triple confidence, source coverage, and schema consistency), KG validation (measuring structural quality, correctness, and completeness), and KG utilization (evaluating retrieval coverage, faithfulness, and query cost). This enables accurate mapping of failures to specific, tunable components. The framework supports both online unsupervised diagnosis in gold-label-free settings and offline supervised calibration when ground truth is available, and is accompanied by a formal theoretical foundation, a proof-of-concept implementation, and a reproducible evaluation protocol.
0 citationsRead paper
I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
Existing methods struggle to disentangle aliased degradation mechanisms in multi-sensor systems and fail to quantify uncertainty in health indicators (HIs). To address this, we propose an input-grouping-driven HI construction framework. First, we introduce projection-path reconstruction into HI generation—integrated with Monte Carlo Dropout and probabilistic latent space modeling—to jointly quantify perceptual (data-driven) and cognitive (model-driven) uncertainties. Second, we propose a sensor-subset grouping paradigm that enables mechanism-specific degradation modeling by leveraging domain knowledge. Evaluated on aerospace and manufacturing system datasets, our method significantly improves remaining useful life (RUL) prediction accuracy and cross-domain generalizability. Moreover, it provides interpretable insights into failure evolution pathways through uncertainty-aware HI trajectories and grouped sensor contributions.
0 citationsRead paper
Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation
Under energy transition, high renewable penetration and cross-border interconnections intensify grid uncertainty, rendering conventional power flow solvers inadequate for real-time operation, while purely data-driven models lack physical consistency. This paper proposes a physics-informed neural network (PINN) framework for power flow simulation, integrating Kirchhoff’s laws and other physical priors via hybrid modeling—combining MLP/GNN architectures with physics-constrained regularization and unsupervised loss. We introduce a novel four-dimensional evaluation framework (accuracy, physical consistency, industrial deployability, out-of-distribution generalization) and the LIPS benchmark platform. Systematic ablation studies demonstrate that explicit graph-structured modeling and direct optimization of physical equations are critical to reliability. The resulting model achieves high accuracy, strong physical consistency, robust out-of-distribution generalization, and practical industrial deployment potential. Code is fully open-sourced.
0 citationsRead paper
AUTOPSY: A Framework for Tackling Privacy Challenges in the Automotive Industry
Intelligent connected vehicles (ICVs) face urgent practical challenges in enhancing privacy-friendliness beyond mere GDPR compliance. Method: This study proposes the first privacy engineering framework tailored for full-vehicle systems, integrating system modeling, a dynamic privacy manager, a GDPR principle–guided PETs selection methodology, and a layered privacy architecture to enable fine-grained data-flow control and automated compliance mapping. The modular design decouples privacy functionality for scalable deployment. A prototype is implemented and validated in a location-based service scenario. Contribution/Results: The framework ensures end-to-end controllability over user data collection, transmission, and processing; achieves privacy-policy response latency under 200 ms; and improves PETs configuration coverage by 40%. It establishes a technically advanced, regulation-adaptive privacy enhancement paradigm for automotive systems.
0 citationsRead paper