隐私计算与数据要素 · 全部实体（第 94 / 100 页）

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• A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.
  europepmc 2025 置信度 0.80
• Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties
  europepmc 2024 置信度 0.80
• Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data.
  europepmc 2024 置信度 0.80
• Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem
  europepmc 2024 置信度 0.80
• Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?
  europepmc 2024 置信度 0.80
• Splitting chemical structure data sets for federated privacy-preserving machine learning.
  europepmc 2021 置信度 0.80
• Swarm Learning as a privacy-preserving machine learning approach for disease classification
  europepmc 2020 置信度 0.80
• Efficient Privacy-preserving Machine Learning in Hierarchical Distributed System.
  europepmc 2019 置信度 0.80
• FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation.
  Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions [1], has…
  pubmed Gu H, Luo J, Kang Y, Yao Y 等 2026 置信度 0.82
• A machine learning framework for early warning prediction of student success using privacy preserving synthetic educational data.
  europepmc 2026 置信度 0.80
• Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction.
  europepmc 2026 置信度 0.80
• Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.
  europepmc 2026 置信度 0.80
• Trustworthy AI for Marketing Measurement: A Systematic Review of Attribution, Media Mix Modeling, and Privacy-Preserving Methods
  europepmc 2026 置信度 0.80
• FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.
  Background/Objectives : The precise identification of Alzheimer's disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phase…
  pubmed Gasmi K, Ammar LB, Krichen M, Alghuried A 2026 置信度 0.82
• Asynchronous proximal federated aggregation for heterogeneous healthcare networks.
  The deployment of Federated Learning (FL) across the Internet of Medical Things (IoMT) is severely hindered by computational asymmetry and statistical heterogeneity. Traditional synchronous aggregation protocols suffer from severe straggler effects when deploy…
  pubmed Sreelakshmi M, Delhibabu R 2026 置信度 0.82
• Privacy Preserving Decentralized Learning With Positive-Incentive Noise.
  europepmc 2026 置信度 0.80
• Machine learning-driven data perturbation techniques for privacy-preserving data mining.
  europepmc 2026 置信度 0.80
• Privacy preservation of EHR by data anonymization and federated learning for IoT based smart city application in healthcare.
  This paper addresses privacy and security challenges in smart cities, particularly in IoT applications that process sensitive data in sectors like healthcare and agriculture. Traditional security solutions often fail to meet the energy and resource constraints…
  pubmed Shakeer SM, Rajasekhara Babu M 2026 置信度 0.82
• Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation.
  Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Synd…
  pubmed Ghazy HE, Ali ZH, Medhat T 2026 置信度 0.82
• Multi-View Clustering Goes Federated: A Survey
  europepmc 2026 置信度 0.80
• Secure, Privacy-Preserving and Revenue-Aware Orchestration of AI-Native 6G Radio Access Networks
  europepmc 2026 置信度 0.80
• A Federated Learning Framework for Privacy-Preserving Predictive Maintenance in Distributed Smart Manufacturing
  europepmc 2026 置信度 0.80
• Privacy-Preserving Model Transcription With Differentially Private Synthetic Distillation.
  europepmc 2026 置信度 0.80
• Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data
  Abstract In decentralized pharmaceutical manufacturing, developing robust predictive models for crystallization is often hindered by proprietary data silos and the inherent heterogeneity of site-specific process dynamics. Standard machine learning approaches s…
  europepmc Sai Vinay Thattukolla 2026 置信度 0.80
• Applications of Machine Learning for Content Recommendation in Social Media Platforms
  europepmc 2026 置信度 0.80
• Automatic detection of multi-site oral squamous cell carcinoma based on privacy-preserving federated learning.
  BACKGROUND: Automated detection of oral squamous cell carcinoma (OSCC) using computer-aided technology plays a crucial role in ensuring patient health and reducing medical costs. However, traditional centralized disease recognition methods often face challenge…
  pubmed Wu Y, Chang H 2026 置信度 0.82
• A Secure Cloud-Based Framework for Privacy-Preserving Medical Pre-Diagnosis Using Encrypted Machine Learning
  europepmc 2026 置信度 0.80
• Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.
  Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that traditional hypothesis-driven approaches cannot address. This review systematically synthesizes research prog…
  pubmed Wu X, Feng J 2026 置信度 0.82
• Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.
  Federated learning enables multiple autonomous vehicles (AVs) to collaboratively train machine learning models while preserving data privacy. However, performance degrades significantly under non-independent and identically distributed (non-IID) data condition…
  pubmed Dhanavarshini V, Periyasamy S 2026 置信度 0.82
• Federated MobileNetV2 with ensemble meta-learning for privacy-preserving brain tumor classification.
  The identification of brain tumors from MRI images is very crucial for the selection of an appropriate treatment. However, the existing solution has issues with privacy and data sharing. To address this challenge, this paper proposes the use of federated learn…
  pubmed Paul M, Tiwari A, Barmashe B, Rai K 等 2026 置信度 0.82
• HeFLDPB: A Trustworthy Triple-Defence Architecture for Privacy-Preserving Collaborative Healthcare Fraud Detection
  europepmc 2026 置信度 0.80
• Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.
  Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. C…
  pubmed Rajput K, Zuberi S, Elhajj M, Ochieng W 等 2026 置信度 0.82
• FL-SDMN: A Federated Learning-Based Software-Defined Mobile Networking for Intelligent Handover Optimization
  europepmc 2026 置信度 0.80
• Multi-modal personalized federated learning with adaptive differential privacy for medical image classification and a privacy-preserving approach.
  Deep learning on medical images classification intervention needs to use large data on multi-institutional datasets but privacy laws inhibit sharing of data (GDPR, HIPAA). Federated Learning (FL) facilitates collaborative training without data transfer; until …
  pubmed M AS, Chowdhary CL 2026 置信度 0.82
• Federated deep reinforcement learning enabled hierarchical Edge-Fog-Cloud architecture for intelligent task offloading in 6G networks.
  Latency sensitive, computation intensive and mobility aware applications in Edge Fog Cloud environments have increased the demand to develop intelligent offloading task mechanism that can dynamically scale to changing network conditions whilst remaining scalab…
  pubmed Ambika B, Pandian SMV, Sundaravadivel P, Vinothkumar ES 2026 置信度 0.82
• A federated learning-benchmarking framework for privacy-preserving UAV intrusion detection using adaptive aggregation algorithms.
  The recent explosive growth of Unmanned Aerial Vehicles (UAVs) has contributed to their high vulnerability to cyber-attacks including Denial of Service (DoS), identity impersonation and unauthorized access to data. The UAV networks have the inherent risks of c…
  pubmed Bithi M, Alsubait T, Ibraheem A, Masud ME 等 2026 置信度 0.82
• A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security.
  europepmc 2026 置信度 0.80
• Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.
  Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. However, existing methods such as Fed-Transformer, Res-1D CNN, and Fed-ESD suffer from privacy risks, inefficient fe…
  pubmed Arulmurugan VS, Aarthy R, Vidhya SS, K S 2026 置信度 0.82
• MontageFL: Privacy-Preserving Federated Learning across Heterogeneous EEG Montages A Leakage-Free Benchmark on Alzheimer’s Disease Classification
  europepmc 2026 置信度 0.80
• BlindU: Blind Machine Unlearning Without Revealing Erasing Data.
  europepmc 2026 置信度 0.80
• A Simple, Privacy-Preserving Multimodal Stacking System for Clinical Progression Prediction in Low-Resource Rare Diseases
  europepmc 2026 置信度 0.80
• Automating differentially private tabular data synthesis via Bayesian optimization.
  europepmc 2026 置信度 0.80
• Machine Unlearning Based on Globally Refined Convergent Clustering for Health Survey Data-driven Prediction Model.
  europepmc 2026 置信度 0.80
• A Scoping Review of Machine Learning and Deep Learning Methods for Autism Spectrum Disorder Diagnosis and Analysis.
  Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine lea…
  pubmed K SR, Y LA 2026 置信度 0.82
• Privacy-preserving multimodal federated learning pipeline for cyber-resilient healthcare systems.
  The integration of Internet of Things (IoT) devices and electronic medical records (EMRs) has transformed healthcare delivery but has also created new vulnerabilities to cyberattacks that threaten both data confidentiality and patient safety. Conventional cent…
  pubmed Tanvir MIM, Rabby HR, Arif MH, Nadia NY 等 2026 置信度 0.82
• Experimental Validation of a Federated Explainable Multi-Modal Transformer–GAN Framework with AutoML-Optimized Ensemble Learning for Bias-Resilient, Privacy-Preserving and Resource-Efficient Real-Time Disease Prediction Across Heterogeneous Multi-Site Populations
  europepmc 2026 置信度 0.80
• Dual-Layer PSO-Enhanced Federated Heterogeneous Data Fusion for Hemodialysis Complication Prediction
  europepmc 2026 置信度 0.80
• Internet of Things-Driven Smart Furniture Systems for Human-Centered Personalization: Experimental Evaluation Using Behavioral Sensor Data.
  This paper introduces an advanced Internet of Things (IoT)-driven smart furniture system designed to dynamically adapt to individual users by integrating deep reinforcement learning with federated meta-learning. Personalization is formulated as a Markov decisi…
  pubmed Wang S 2026 置信度 0.82
• Hierarchical proof of trust a Byzantine fault tolerant federated learning framework for industrial IoT applications.
  The Industrial Internet of Things (IIoT) presents significant challenges for training machine learning models due to data privacy concerns, heterogeneous data distributions, and limited bandwidth. This paper proposes HPoT (Hierarchical Proof-of-Trust), a novel…
  pubmed Chaurasia A, Sharma SK, Rathore PS 2026 置信度 0.82
• Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence
  europepmc 2026 置信度 0.80
• DL-DPGAN: A Correlation-Regularized Differentially Private GAN for Privacy-Utility Balanced Synthetic Data Generation
  europepmc 2026 置信度 0.80
• Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.
  europepmc 2026 置信度 0.80
• Resource-efficient federated machine unlearning via evolutionary synaptic pruning for cloud-based distributed learning systems.
  With an increasing emphasis on user data control and privacy regulations, such as the General Data Protection Regulation (GDPR), machine unlearning (MU) has emerged as a crucial mechanism for managing data in AI systems. MU enables models to remove the influen…
  pubmed Bansal H, Unhelkar B, Saini DKJB, Rai BK 等 2026 置信度 0.82
• A Personalized and Privacy-Preserving Federated Transformer Framework for Multilingual Sentiment Analysis.
  europepmc 2026 置信度 0.80
• Blockchain-enhanced federated learning for IoT security and privacy using the GSR-C2N model.
  The rapid proliferation of Internet of Things devices has intensified the demand for security, privacy-preserving, and scalable machine learning solutions. Federated Learning (FL) supports the decentralized training of models across distributed devices without…
  pubmed Ahmad S, Ansari MA, Mewada A, Ahmed G 2026 置信度 0.82
• Poison-Resilient and Privacy-Preserving Federated Learning Scheme in Mobile Systems
  Abstract Mobile systems including smartphones, IoT devices generate massive high value data, while conventional centralized data collection and analysis suffer from security and privacy vulnerabilities. Federating learning, an emerging paradigm in machine lear…
  europepmc Quanyu Zhao, Chenrui Gu, Bingbing Jiang, Yuanjian Zhou 等 2026 置信度 0.80
• Age-stratified machine learning using de-identified clinical and transcriptomic data for pediatric appendicitis.
  europepmc 2026 置信度 0.80
• Novel indicator for colonoscopy insertion difficulty and its application in predictive modelling: a multicentre prospective study protocol in China.
  Background Colonoscopy is a cornerstone for the screening and diagnosis of colorectal diseases. Colonoscopy insertion difficulty is associated with examination quality, procedural efficiency and patient comfort. Cecal intubation time (CIT) is commonly used as …
  europepmc 2026 置信度 0.80
• CITADEL: a post-quantum secure blockchain framework for privacy-preserving electronic health records with temporally-partitioned federated learning.
  Electronic health records (EHRs) increasingly anchor clinical decision support and population-scale analytics, yet their concentration of sensitive information amplifies disclosure risk, widens the attack surface, and faces emerging threats from quantum comput…
  pubmed Segar N, Vijayan V 2026 置信度 0.82
• Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning
  Background: /Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysi…
  europepmc 2026 置信度 0.80
• FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.
  Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature of healthcare data and the need for privacy-preserving solutions. This paper proposes FEDI-CODE, a Federated …
  pubmed Moniruzzaman M, Uddin MS, Ahmed A, Aktarujjaman M 等 2026 置信度 0.82
• Automating standardization of prostate cancer biopsy and histopathology reports with privacy-preserving local large language models.
  europepmc 2026 置信度 0.80
• Customer churn prediction in privacy-preserving HashCode-based security abstractions.
  europepmc 2026 置信度 0.80
• Privacy-Preserving Diabetes Prediction Using Federated Learning in Edge-Based Healthcare
  europepmc 2026 置信度 0.80
• Federated high order tensor fusion for privacy preserving multimodal social media analysis.
  The rapid evolution of social networks has positioned multimodal content, including text, images, and audio, as a pivotal medium for self-expression and public sentiment analysis. However, existing multimodal fusion methods are often limited by privacy risks, …
  pubmed Wan L, Zhang B 2026 置信度 0.82
• Aligning Condensed Graph via Hashing: A New Insight for Federated Graph Learning.
  Federated Graph Learning (FGL) aims to maximize the benefits of each graph owner, which is a common form of distributed graph learning under privacy-preserving conditions. As the landscape of local clients becomes increasingly diverse in terms of both model ar…
  pubmed Yan Y, Zheng S, Zhu Z, Chen D 等 2026 置信度 0.82
• Privacy-Preserving Hybrid GA-LSTM Ensemble for Typhoid Detection Using Optimised Clinical Feature Selection.
  Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to devel…
  pubmed Gasmi K, Alanazi A, Almenwer S, Almaghrabi S 等 2026 置信度 0.82
• FMLCA: explainable and privacy-preserving federated machine learning classification algorithms for predicting heart disease in patients.
  europepmc 2026 置信度 0.80
• Smart Livestock: A Federated Learning Based System for Real-Time Cattle Health, Stress and Water Resource Monitoring.
  Centralized machine learning (ML) models often face challenges related to data privacy, messaging latency and limited internet access in rural areas. To address these issues, a federated learning (FL) based architecture for real-time detection of health and st…
  pubmed Gulati V, Grover R, Kumar N, Jindal P 等 2026 置信度 0.82
• Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.
  europepmc 2026 置信度 0.80
• Introduction to TinyML: The New Era of Low-Power AI for IoT Devices
  europepmc 2026 置信度 0.80
• Machine Learning-Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi-Media Systems.
  Machine learning (ML) holds promise for reconstructing microplastic (MP) aging and assessing risks, but current studies rely on small-scale, accelerated laboratory datasets and single environmental medium models that miss cross-media transport and environmenta…
  pubmed Lyu Y, Qiu X, Li X, Yang T 等 2026 置信度 0.82
• Artificial intelligence for child health: current capabilities and the next frontier.
  Artificial intelligence (AI) is reshaping paediatric healthcare, offering new capabilities across diagnosis, monitoring and treatment personalisation. Modern AI systems integrate multimodal data, including imaging, genomics, electronic health records, wearable…
  pubmed Dimitri P 2026 置信度 0.82
• Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.
  europepmc 2026 置信度 0.80
• Real-Time Financial Fraud Detection: A Systematic Review of Big Data Architectures, Machine Learning Models, and Adaptive Intelligence
  europepmc 2026 置信度 0.80
• From prediction to practice: Barriers to implementing artificial intelligence in blood inventory management and transfusion support.
  europepmc 2026 置信度 0.80
• A systematic review of machine learning approaches for phonocardiogram classification.
  Recent advances in machine learning (ML) have accelerated automated analysis of phonocardiogram (PCG) signals, yet prior surveys often narrow their scope (e.g. omitting segmentation, focusing only on classical ML or overlooking recent deep learning [DL] trends…
  pubmed Al-Shanoon A, Sykes ER, Nazari H 2026 置信度 0.82
• Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.
  europepmc 2026 置信度 0.80
• BRFedTD: A Novel Framework for Federated Reinforcement Learning With Byzantine-Resilient Policy Evaluation.
  Federated reinforcement learning (FRL) enables distributed agents to collaboratively evaluate policies without sharing raw data, making it a promising approach for privacy-preserving and scalable decision-making. However, the presence of Byzantine agents, whic…
  pubmed Wang D, Liu W, Mao X 2026 置信度 0.82
• Adaptive feature unlearning for trustworthy medical imaging privacy.
  europepmc 2026 置信度 0.80
• Adversarial-resilient lightweight phishing url detection: Evaluating lexical & metadata features under evasion techniques.
  europepmc 2026 置信度 0.80
• Engineering Privacy-Preserving Machine Learning Protocols
  crossref Thomas Schneider 2020-11-04T03:22:57Z 置信度 0.70
• MPC-enabled privacy-preserving machine learning
  crossref Ziyao Liu 2023-06-01T06:29:45Z 置信度 0.70
• Privacy-preserving machine learning techniques
  Apprentissage automatique et confidentialité des données L'apprentissage automatique en tant que service (MLaaS) fait référence à un service qui permet aux entreprises de déléguer leurs tâches d'apprentissage automatique à un ou plusieurs serveurs puissants, à…
  crossref Beyza Bozdemir 2026-04-06T13:11:38Z 置信度 0.70
• Accelerating privacy-preserving machine learning with genibatch
  crossref Xinyang Huang 2023-09-21T22:45:02Z 置信度 0.70
• Privacy preserving and efficient machine learning algorithms
  Η ευρεία διαθεσιμότητα δεδομένων έχει αποτελέσει καταλύτη για την ανάπτυξη της βαθιάς μάθησης. Οι εξελίξεις αυτές περιλαμβάνουν την ταξινόμηση εικόνων, την αναγνώριση ομιλίας και την επεξεργασία φυσικής γλώσσας. Ωστόσο, η πρόοδος που βασίζεται στα δεδομένα συχ…
  crossref Ευσταθία Σουφλέρη 2025-05-12T08:53:09Z 置信度 0.70
• Cryptography for privacy-preserving machine learning
  La cryptographie au service de l'apprentissage automatique respectueux de la vie privée L’usage sans précédent du machine learning (ML) ou apprentissage automatique, motivé par les possibilités qu’il apporte dans un grand nombre de secteurs, interroge de plus …
  crossref Théo Ryffel 2026-04-07T04:35:20Z 置信度 0.70
• Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments
  crossref Bangar Raju Cherukuri 2024-10-25T08:48:08Z 置信度 0.70
• Robust and privacy preserving distributed machine learning
  Apprentissage automatique distribué robuste et respectueux de la vie privée Avec l’omniprésence des services numériques, d’´énormes quantités de données sont continuellement générées et collectées. Les algorithmes d’apprentissage automatique (ML) permettant d’…
  crossref Rania Talbi 2026-04-07T03:14:23Z 置信度 0.70
• Privacy-Preserving Machine Learning over Distributed Data
  crossref Ali Reza Ghavamipour 2024-11-06T12:18:26Z 置信度 0.70
• Privacy Preserving Machine Learning as a Service
  Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this is…
  crossref Ehsan Hesamifard 2025-02-08T09:01:31Z 置信度 0.70
• Machine Learning in Medical Diagnostics: Integrating Algorithms, Clinical Workflows, and Privacy-Preserving Strategies
  crossref Yiran Qiu 2026-07-19T11:49:27Z 置信度 0.70
• Figure 1: Flowchart of the proposed privacy-preserving machine learning framework.
  crossref 2026-07-23T08:24:22Z 置信度 0.70
• Privacy-preserving computations and machine learning over decentralized data
  Στη σύγχρονη εποχή, οι αναπτυσσόμενοι τομείς της μηχανικής μάθησης (Machine Learning) και της τεχνητής νοημοσύνης (Artificial Intelligence) έχουν επιφέρει ραγδαία τεχνολογική πρόοδο, αλλάζοντας τον τρόπο προσέγγισης και επίλυσης σύνθετων προβλημάτων. Οι ευρέως…
  crossref Βασίλειος Περηφάνης 2024-05-28T06:54:01Z 置信度 0.70
• Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning
  Approches combinant confidentialité différentielle et chiffrement homomorphe pour la protection des données en apprentissage automatique collaboratif L'objet de cette thèse est la conception de protocoles pour l'entraînement de modèles d'apprentissage automati…
  crossref Arnaud Grivet Sébert 2026-04-07T16:40:32Z 置信度 0.70
• Inherent trade-offs in privacy-preserving machine learning
  Compromis inhérents à l'apprentissage automatique préservant la confidentialité À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la c…
  crossref Caelin Kaplan 2026-04-08T23:02:04Z 置信度 0.70
• Privacy-Preserving Machine Learning
  crossref 2025-01-10T20:16:08Z 置信度 0.70
• Privacy-Preserving Mechanisms for SVM Learning
  crossref 2019-03-14T03:14:16Z 置信度 0.70
• Exploiting problem structure in privacy-preserving optimization and machine learning
  Exploitation de la structure des problèmes en optimisation et en apprentissage automatique respectueux de la vie privée Au cours des dernières décennies, les préoccupations quant à l'impact sociétal de l'apprentissage automatique se sont multipliées. En effet,…
  crossref Paul Mangold 2026-04-08T07:54:02Z 置信度 0.70
• Innovative Protocols for Decentralized Financial Systems and Privacy-Preserving Machine Learning
  crossref Zhenhang Shang 2026-01-15T23:00:44Z 置信度 0.70