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cryptography and security cs.cr

GeneTech 知识引擎共收录 298 篇相关科研实体，横跨 15 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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• Decentralized Granular Access Control for Agentic AI Systems in Critical Infrastructure
  2026 · AI Agent 生态 · The deployment of autonomous AI agents in production infrastructure introduces fundamental security challenges that traditional role-based access control (RBAC) models cannot address. Unlike deterministic automation, AI …
• Redakto - The Incognito Tab for LLMs
  2026 · 医疗人工智能 · Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (AI) in general is to ensure privacy when using them, meaning that pe…
• Blockchain Infrastructure for Intelligent Cyber--Physical--Social Systems:Post-Quantum Security, Interoperability, and Trustworthy Data Economies in the Era of Embodied AI
  2026 · 具身智能 · The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-org…
• Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
  2026 · 具身智能 · Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter do…
• Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise
  2026 · 具身智能 · Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task planning for both single robot and multi-robot collaboration. This incr…
• A Survey of Security Challenges and Solutions for Advanced Air Mobility and eVTOL Aircraft
  2026 · 低空经济 · This survey reviews the existing and envisioned security vulnerabilities and defense mechanisms relevant to Advanced Air Mobility (AAM) systems, with a focus on electric vertical takeoff and landing (eVTOL) aircraft. Dra…
• Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets
  2026 · 隐私计算与数据要素 · Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A p…
• Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
  2026 · 隐私计算与数据要素 · Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant a…
• SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence
  2026 · 隐私计算与数据要素 · The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direc…
• Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
  2026 · 隐私计算与数据要素 · Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explo…
• FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
  2026 · 隐私计算与数据要素 · Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryp…
• Game-Theoretic Framework for Private Data Sharing in Vehicular Networks
  2026 · 隐私计算与数据要素 · We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems. The proposed hybrid architecture comprises vehicles that supply sensor data, ind…
• Secure and Privacy-Preserving Vertical Federated Learning
  2026 · 隐私计算与数据要素 · We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in feder…
• EncFormer: Secure and Efficient Transformer Inference over Encrypted Data
  2026 · 隐私计算与数据要素 · Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (FHE) and secure multiparty computation (…
• Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap
  2026 · 隐私计算与数据要素 · The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constra…
• Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions
  2026 · 隐私计算与数据要素 · Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model co…
• MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics
  2026 · 隐私计算与数据要素 · Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. Our FL framework is designed to address t…
• Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks
  2026 · 隐私计算与数据要素 · Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In this paper, we study a semantics-driven backdoor mechanism in which att…
• A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction
  2026 · 隐私计算与数据要素 · This study explores privacy-preserving machine learning (PPML) techniques using the PySyft platform to enable collaborative prediction of student retention between institutions. We developed a remote data science (RDS) f…
• Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
  2026 · 隐私计算与数据要素 · Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deployable system must satisfy three requirements simultaneously: legal-grade…
• Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning
  2026 · 隐私计算与数据要素 · Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and e…
• QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
  2026 · 隐私计算与数据要素 · Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, …
• Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
  2026 · 隐私计算与数据要素 · Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: c…
• Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces
  2026 · 隐私计算与数据要素 · The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe gover…
• AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation
  2026 · 隐私计算与数据要素 · In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collaps…
• ATLAS: Automated Approximation of Transformers for Efficient Homomorphic Inference in One Hour
  2026 · 隐私计算与数据要素 · Fully homomorphic encryption (FHE) lets a server run inference on encrypted data with strong privacy guarantees, but running a Transformer under FHE is expensive. Its non-linear operations, such as softmax, normalization…
• Latency-Optimal Adaptive Split Inference for Privacy-Preserving Cloud-Edge-End Collaboration
  2026 · 隐私计算与数据要素 · Internet of Things (IoT) end devices are increasingly expected to support privacy-sensitive batch inference, yet their limited computational resources often make full local execution of convolutional neural networks impr…
• TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems
  2026 · 隐私计算与数据要素 · Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evaluations to at most ~20k nodes and making them incompatible with dynamic…
• Practical Anonymous Two-Party Gradient Boosting Decision Tree
  2026 · 隐私计算与数据要素 · Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High speed and interpretability make GBDTs popu…
• FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding
  2026 · 隐私计算与数据要素 · Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead. These costs come not only from expensive low-level primitives, including Number Theoret…
• On the (non-)resilience of encrypted controllers to covert attacks
  2026 · 隐私计算与数据要素 · The security of networked control systems (NCS) is receiving increasing attention from both cyber-security and system-theoretic perspectives. The former focuses on classical IT security goals such as confidentiality, int…
• DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
  2026 · 隐私计算与数据要素 · Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect privacy against an honest-but-curious se…
• Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption
  2026 · 隐私计算与数据要素 · The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Traditional encryption methods protect data at rest or in transit but fa…
• Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval
  2026 · 隐私计算与数据要素 · Retrieval Augmented Generation (RAG) systems deployed across organizational boundaries face fundamental tensions between security, accuracy, and efficiency. Current encryption methods expose plaintext during decryption, …
• AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
  2026 · 隐私计算与数据要素 · Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because encoded weights are already large and encryp…
• Meeting in the Middle: A Co-Design Paradigm for FHE and AI Inference
  2026 · 隐私计算与数据要素 · Modern cloud inference creates a two sided privacy problem where users reveal sensitive inputs to providers, while providers must execute proprietary model weights inside potentially leaky execution environments. Fully h…
• Decentralized Privacy-Preserving Federal Learning of Computer Vision Models on Edge Devices
  2026 · 隐私计算与数据要素 · Collaborative training of a machine learning model comes with a risk of sharing sensitive or private data. Federated learning offers a way of collectively training a single global model without the need to share client d…
• mmFHE: mmWave Sensing with End-to-End Fully Homomorphic Encryption
  2026 · 隐私计算与数据要素 · We present mmFHE, the first system that enables fully homomorphic encryption (FHE) for end-to-end mmWave radar sensing. mmFHE encrypts raw range profiles on a lightweight edge device and executes the entire mmWave signal…
• Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spots
  2026 · 隐私计算与数据要素 · Memorization in large language models is measured through a zoo of definitions whose formal relations are unknown, and differential privacy (DP) is treated as a proxy against all of them at once. We pin down the exact DP…
• From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems
  2026 · 机器人零部件 · As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can propagate through the planning pipeline to physical-world consequences…
• Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks
  2026 · 6G 与卫星互联网 · The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond. This study introduces an advan…
• ISAC Privacy: Challenges and Solutions for 6G
  2026 · 6G 与卫星互联网 · Integrated sensing and communication (ISAC) is a promising feature of future communication networks. While spatial sensing can improve network performance and enable external services, it also creates privacy challenges …
• Secure Integrated Sensing and Communication: Information Theory Offers Insights
  2026 · 6G 与卫星互联网 · Integrated sensing and communication (ISAC) combines sensing and communication within a shared system framework by using the same transmitted signal for both objectives. ISAC can improve the efficiency of spectrum and ha…
• Nanoelectromechanical Systems (NEMS) for Hardware Security in Advanced Packaging
  2026 · 半导体与先进封装 · As hardware security threats escalate across semiconductor manufacturing and advanced packaging, there is a growing need for novel physical mechanisms to counter sophisticated attacks such as tampering, counterfeiting, a…
• Smoothed Embeddings for Robust Language Models
  2025 · AI 安全与对齐 · Improving the safety and reliability of large language models (LLMs) is a crucial aspect of realizing trustworthy AI systems. Although alignment methods aim to suppress harmful content generation, LLMs are often still vu…
• An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems
  2025 · 边缘智能 · Radiation Detection Systems (RDSs) are used to measure and detect abnormal levels of radioactive material in the environment. These systems are used in many applications to mitigate threats posed by high levels of radioa…
• Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
  2025 · 边缘智能 · Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings, from nuclear facilities to medical environments. However, these systems are increasingly vulnerable to cyber-attacks…
• Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
  2025 · 边缘智能 · Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge devices. However, a notable drawback of DNN…
• An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing
  2025 · 低空经济 · This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility (UAM) vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolut…
• A Robust Federated Learning Approach for Combating Attacks Against IoT Systems Under non-IID Challenges
  2025 · 隐私计算与数据要素 · In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machin…
• POPri: Private Federated Learning using Preference-Optimized Synthetic Data
  2025 · 隐私计算与数据要素 · In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperforme…
• Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
  2025 · 隐私计算与数据要素 · Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on …
• Controlled privacy leakage propagation throughout overlapping grouped learning
  2025 · 隐私计算与数据要素 · Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself l…
• A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
  2025 · 隐私计算与数据要素 · The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due…
• Watch Out for the Lifespan: Evaluating Backdoor Attacks Against Federated Model Adaptation
  2025 · 隐私计算与数据要素 · Large models adaptation through Federated Learning (FL) addresses a wide range of use cases and is enabled by Parameter-Efficient Fine-Tuning techniques such as Low-Rank Adaptation (LoRA). However, this distributed learn…
• Federated Learning With Individualized Privacy Through Client Sampling
  2025 · 隐私计算与数据要素 · With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a un…
• Cheat-Penalised Quantum Weak Coin-Flipping
  2025 · 隐私计算与数据要素 · Coin-flipping is a fundamental task in two-party cryptography where two remote mistrustful parties wish to generate a shared uniformly random bit. While quantum protocols promising near-perfect security exist for weak co…
• Poison to Detect: Detection of Targeted Overfitting in Federated Learning
  2025 · 隐私计算与数据要素 · Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to …
• FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
  2025 · 隐私计算与数据要素 · Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated priva…
• An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
  2025 · 隐私计算与数据要素 · Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such…

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