隐私计算与数据要素 · 全部实体（第 95 / 100 页）

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• Trustful AI through fair and privacy-preserving federated machine learning
  Intelligence artificielle digne de confiance grâce à l'apprentissage fédéré équitable et respectueux de la vie privée L'augmentation massive du volume de données disponibles a transformé l'informatique moderne, contribuant à l'essor de l'apprentissage automati…
  crossref Saida Benarba 2026-07-22T14:28:08Z 置信度 0.70
• Privacy-Preserving Isolated-Word Recognition
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Privacy-preserving machine learning for large-scale collaborative healthcare data analysis
  Apprentissage automatique sécurisé pour l'analyse collaborative des données de santé à grande échelle Cette thèse de doctorat explore l'intégration de la préservation de la confidentialité, de l'imagerie médicale et de l'apprentissage fédéré (FL) à l'aide de m…
  crossref Riccardo Taiello 2026-04-08T20:21:54Z 置信度 0.70
• Resilient Privacy Preserving Machine Learning for Internet of Things
  The rapid development of the Internet of Things (IoT) presents new challenges (such as privacy concerns) to existing data analytics frameworks deployed in IoT-based applications. Federated learning (FL), a type of distributed and privacy-preserving learning fr…
  crossref Kun Yang, Neena Imam 2025-04-28T15:42:57Z 置信度 0.70
• Efficient and robust protocols for privacy-preserving semi-decentralized machine learning
  Protocoles efficaces et robustes pour l'apprentissage automatique semi-décentralisé préservant la confidentialité Ces dernières années, la préoccupation pour la protection de la vie privée s'est considérablement accrue. Cela s'explique par l'utilisation réguli…
  crossref César Sabater 2026-04-07T04:28:20Z 置信度 0.70
• Privacy-Preserving Data Mining
  crossref 2010-12-29T17:26:04Z 置信度 0.70
• Secure and Privacy-Preserving Machine Learning
  crossref Sergio Barezzani 2025-01-10T20:20:57Z 置信度 0.70
• Privacy-preserving quantum machine learning using differential privacy
  crossref Makhamisa Senekane, Mhlambululi Mafu, Benedict Molibeli Taele 2017-11-28T16:04:21Z 置信度 0.70
• Privacy-Preserving Machine Learning [Cryptography]
  crossref Florian Kerschbaum, Nils Lukas 2023-11-13T19:42:58Z 置信度 0.70
• Fortifying Machine Learning, Data Privacy, and Secure Collaboration
  The utilitarianism of machine learning (ML) techniques introduces an additional layer of security to ML applications. In the domain of cybersecurity, ML continuously evolves by analyzing data to identify patterns, thus enhancing the capacity to detect malware …
  crossref P. Shyamala Madhuri, B. Amutha, D. J. Nagendra Kumar 2024-05-31T08:05:21Z 置信度 0.70
• An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning
  crossref Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, Christian Peluso 等 2024-05-15T13:10:07Z 置信度 0.70
• Artificial Intelligence and Machine Learning in Healthcare: Developing Privacy-Preserving Frameworks
  crossref Ugochukwu Echendu, Chidiebere Udeokechukwu 2025-03-25T19:17:47Z 置信度 0.70
• Local Differential Privacy for Privacy-preserving Machine Learning
  crossref Cormode Graham 2026-08-04T15:34:34Z 置信度 0.70
• Zero-Knowledge Proofs for Machine Learning
  crossref Yupeng Zhang 2020-11-04T03:22:57Z 置信度 0.70
• Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice
  crossref 2020-11-04T03:22:57Z 置信度 0.70
• Privacy-Preserving Medical Text Data Publishing with Machine Learning
  crossref Tanbir Ahmed, Noman Mohammed 2022-03-25T14:52:38Z 置信度 0.70
• Privacy-Preserving Speaker Verification as String Comparison
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• SecureML: A System for Scalable Privacy-Preserving Machine Learning
  crossref Payman Mohassel, Yupeng Zhang 2017-06-26T20:34:26Z 置信度 0.70
• Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods
  crossref Yonan Yonan, Mohammad Abdullah, Felix Nilsson, Mahdi Fazeli 等 2025-06-24T11:37:04Z 置信度 0.70
• Privacy-Preserving Speaker Identification as String Comparison
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Privacy-Preserving Vital node Identification in Complex Networks using Machine Learning
  Abstract Identifying vital nodes in complex networks is critical in various research areas, including social network analysis, epidemiology, and physics. Centrality measures are commonly used and combined for this purpose. However, the impact of growing privac…
  crossref Diaoulé Diallo, Tobias Hecking 2024-02-29T05:09:11Z 置信度 0.70
• Privacy‑Preserving Synthetic Healthcare Data: Machine Learning Applications in Vital Sign Monitoring and Diagnosis
  crossref Sellappan Palaniappan, Kasthuri Subaramaniam, Oras Baker 2026-07-09T17:14:28Z 置信度 0.70
• Privacy Preserving Machine Learning
  crossref Daniel Meier, Juan R. Troncoso Pastoriza 2023-11-06T18:11:37Z 置信度 0.70
• Privacy-Preserving in Defending against Membership Inference Attacks
  crossref Zuobin Ying, Yun Zhang, Ximeng Liu 2020-11-04T03:22:57Z 置信度 0.70
• Privacy-Preserving Speaker Verification Using Gaussian Mixture Models
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Privacy-Preserving Speaker Identification Using Gaussian Mixture Models
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments
  In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human prog…
  crossref Zayyanu Yunusa 2026-05-26T10:55:57Z 置信度 0.70
• Federated Learning Architectures for Privacy Preserving Financial Fraud Detection Systems
  The increasing complexity and intensity of cases of financial fraud, such as synthetic identity fraud and international money laundering, have become significant concerns for classic fraud detection solutions, especially under strict data privacy regulations s…
  crossref Favour . C. Ezeugboaja 2025-12-22T08:49:02Z 置信度 0.70
• Privacy-preserving federated learning with differential privacy for healthcare AI: a convergence and utility analysis
  Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without sharing raw patient data, offering a paradigm shift for privacy-sensitive medical AI. However, FL remains vulnerable to gradient inversion attacks a…
  crossref Aruna Pavate 2026-05-30T06:28:24Z 置信度 0.70
• Privacy-Preserving Machine Learning: ANN Activation Function Estimators for Homomorphic Encrypted Inference
  The rising demand for cloud-based machine learning services has intensified concerns about data privacy, particularly in sensitive fields like healthcare and finance. Homomorphic Encryption (HE) enables computations on encrypted data, offering a promising solu…
  crossref Mhd Raja Abou Harb, Baris Celiktas 2025-01-03T10:46:10Z 置信度 0.70
• Comparison of Fully Homomorphic Encryption and Garbled Circuits approaches in Privacy-Preserving Machine Learning
  Machine Learning (ML) is making its way into fields such as healthcare, finance, and natural language processing (NLP), and concerns over data privacy and model confidentiality continue to grow. Privacy-Preserving Machine Learning (PPML) addresses this challen…
  crossref Kalyan Cheerla 2025-10-14T01:17:21Z 置信度 0.70
• Federated Learning for Privacy-Preserving Smart Cities: A Secure and Scalable Machine Learning Framework
  Abstract The exponential growth of data in smart city infrastructures—from traffic systems to health monitoring and surveillance—has created unprecedented opportunities for machine learning applications. However, centralizing such diverse and sensitive data in…
  preprints Deepak Juneja, Arvinder Singh, Jagvinder Singh 2025 置信度 0.74
• Privacy-Preserving Machine Learning in Healthcare Applications
  The integration of machine learning (ML) in healthcare has unlocked transformative potential in disease prediction, personalized treatment, medical imaging, remote patient monitoring, and genomic data analysis. However, the sensitive nature of medical data int…
  crossref Ravi Mishra, Rushikesh Bankar 2025-06-05T07:51:51Z 置信度 0.70
• Reallocation of privacy harm by privacy-preserving machine learning techniques
  crossref Arijit Goswami 2026-04-27T02:25:12Z 置信度 0.70
• Privacy-Preserving Transfer Learning for Community Detection in Multiple Networks: A Review.
  In order to identify communities in various networks, this study gives a thorough analysis of privacy-preserving transfer learning methods. In order to better understand the specific difficulties of implementing transfer learning in decentralized and diverse s…
  crossref Marshima Mohd Rosli 2025-05-03T06:40:36Z 置信度 0.70
• Privacy Preserving Nearest Neighbor Search
  crossref Mark Shaneck, Yongdae Kim, Vipin Kumar 2009-03-30T14:39:55Z 置信度 0.70
• Privacy-Preserving Machine Learning on Financial Data: Federated Learning, Differential Privacy, and Practical Deployment Challenges in Banking
  Machine learning on financial data sits at an awkward intersection: the data is among the most sensitive in any industry, the regulatory regime is among the strictest, and the business value of better models is large enough to keep pulling new ML workloads int…
  crossref Jeevan Krishna Paruchuri 2026-04-22T18:31:16Z 置信度 0.70
• Applications—Privacy-Preserving Image Processing
  crossref Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen 等 2022-03-14T02:02:37Z 置信度 0.70
• Privacy-Preserving Machine Learning Approach for Real-Time Brain Tumor Diagnosis Using Federated Learning Techniques
  crossref Sathish K, Rajesh Sharma R, Mohit Tiwari, Akey Sungheetha 等 2026-03-04T15:36:38Z 置信度 0.70
• BLAZE: Blazing Fast Privacy-Preserving Machine Learning
  crossref Arpita Patra, Ajith Suresh 2020-02-25T15:02:43Z 置信度 0.70
• Privacy-preserving, Efficient, and Effective Machine Learning
  Abstract Privacy protection is critical for responsible artificial intelligence. Federated learning is a privacy-aware machine learning paradigm, which is often combined with differential privacy to guarantee privacy protection. Unfortunately, existing methods…
  crossref Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang 等 2022-06-23T16:09:39Z 置信度 0.70
• HEaaN: A scalable privacy-preserving machine learning using homomorphic encryption for bioinformatician
  Homomorphic Encryption (HE) is a cryptographic scheme that enables arbitrary computations over encrypted data. It offers perfect protection for data at rest, in use, and in transit because encrypted data can be analyzed without decryption. We introduce HEaaN, …
  crossref Soo-Heang Abel Eo, Song Hyeop Park 2025-11-21T16:42:06Z 置信度 0.70
• Privacy-Preserving Attribute Domain Reconstruction for Machine Learning
  In modern society, the use of personal data is advancing in many fields. However, such data utilization also increases the risk of privacy leakage. Therefore, Differential Privacy (DP) has been proposed as a measure of privacy protection. DP is a privacy-prese…
  crossref Yuto Tsujimoto, Atsuko Miyaji 2025-08-21T22:10:10Z 置信度 0.70
• Verifiable and Privacy-Preserving Decentralized Collaboration for Machine Learning Model Improvement
  Abstract Decentralized collaboration, particularly in complex domains like machine learning (ML) model development, faces hurdles regarding trust, intellectual property (IP) protection, and the verification of contributions. Traditional centralized platforms i…
  crossref Jay Bojič Burgos, Urban Sedlar, Matevž Pustišek 2025-08-30T16:40:17Z 置信度 0.70
• PRIVACY-PRESERVING MACHINE LEARNING IN CYBERSECURITY
  crossref Pranav Mani Tripathi 2024-12-28T07:52:36Z 置信度 0.70
• Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
  crossref Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis 2023-06-01T13:27:45Z 置信度 0.70
• Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards
  crossref Nathan Martindale, Scott Stewart, Mark Adams, Greg Westphal 2020-12-21T03:15:53Z 置信度 0.70
• Federated Learning for Privacy-Preserving AI: A Comparative Analysis of Decentralized Data Training
  The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. T…
  crossref Dr. Subash Ranjan Kabat 2025-04-18T10:30:36Z 置信度 0.70
• Utilizing Generative Adversarial Networks for Preserving Privacy in Developing Machine Learning Models for the Healthcare Industry
  crossref Shahnawaz Khan, Bharavi Mishra, Sultan Alamri, Philippe Pringuet 2025-07-03T11:57:56Z 置信度 0.70
• Differential Privacy-Preserving Algorithms for Secure Training of Machine Learning Models
  crossref Sandeep Phanireddy 2025-06-25T09:42:52Z 置信度 0.70
• Federated Learning: Achieving Scalable and Privacy-Preserving Machine Learning
  crossref Harsh Bansal, Kanu Goel 2024-05-28T17:43:26Z 置信度 0.70
• Privacy Background
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Machine Learning Algorithms for Bitcoin Price Prediction
  crossref Prasannavenkatesan Theerthagiri 2022-06-28T17:31:09Z 置信度 0.70
• Exploring Strategies for Privacy-Preserving Machine Learning in Distributed Environments
  Machine Learning (ML) with distributed privacy preservation is growing in significance as it focuses on facilitating multi-party learning without requiring actual data sharing. This is especially helpful for companies that want to work together but are unable …
  crossref Suresh Dodda, Anoop Kumar, Navin Kamuni, Madan Mohan Tito Ayyalasomayajula 2024-04-29T12:21:37Z 置信度 0.70
• Overview of Speech Recognition with Privacy
  crossref Manas A. Pathak 2012-10-25T01:58:43Z 置信度 0.70
• Federated quantum natural gradient descent for quantum federated learning
  crossref Jun Qi, Min-Hsiu Hsieh 2024-02-23T09:49:46Z 置信度 0.70
• Federated Machine Learning for Systems Medicine
  crossref Richard Röttger 2023-10-09T15:45:25Z 置信度 0.70
• Centralized versus decentralized federated learning
  crossref Irina Arévalo, Jose L. Salmeron 2026-06-12T10:49:31Z 置信度 0.70
• Personalized federated learning strategies
  crossref SangMook Kim 2025-03-07T12:49:20Z 置信度 0.70
• Federated Learning
  crossref Sana Daud 2024-07-18T08:39:48Z 置信度 0.70
• Adversarial robustness in federated learning
  crossref Chulin Xie, Xiaoyang Wang 2024-02-23T09:49:00Z 置信度 0.70
• Federated Learning
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Assessing vulnerabilities and securing federated learning
  crossref Supriyo Chakraborty, Arjun Bhagoji 2024-02-23T09:48:57Z 置信度 0.70
• Introduction to Federated Learning
  crossref Vaneeza Mobin 2024-07-18T08:39:48Z 置信度 0.70
• Graph-aware federated learning
  crossref Songtao Lu, Pengwei Xing, Han Yu 2024-02-23T09:49:14Z 置信度 0.70
• Blockchain-enabled federated learning
  crossref Murtaza Rangwala, K.R. Venugopal, Rajkumar Buyya 2026-06-12T10:49:31Z 置信度 0.70
• Δ SFL: (Decoupled Server Federated Learning) to Utilize DLG Attacks in Federated Learning by Decoupling the Server
  crossref Sudipta Paul, Vicenç Torra 2023-07-13T10:16:41Z 置信度 0.70
• Federated Learning in Healthcare
  crossref Muhammad Hamza 2024-07-18T08:39:48Z 置信度 0.70
• Federated learning at a glance
  crossref Anwesha Mukherjee, Sajal K. Das, Rajkumar Buyya 2026-06-12T10:49:31Z 置信度 0.70
• Meta-federated learning
  crossref Omid Aramoon, Pin-Yu Chen, Gang Qu, Yuan Tian 2024-02-23T09:49:18Z 置信度 0.70
• Federated Reinforcement Learning
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Federated noisy client learning
  crossref Kahou Tam, Huazhu Fu 2025-03-07T12:49:34Z 置信度 0.70
• FedND: Federated Newton Direction for Federated Learning Environment
  crossref 2025-12-13T15:05:58Z 置信度 0.70
• Introduction to Federated Learning
  crossref Alexander Jung 2026-01-02T02:27:43Z 置信度 0.70
• Mobile computing framework for federated learning
  crossref Xiang Chen, Fuxun Yu, Zirui Xu 2024-02-23T09:49:52Z 置信度 0.70
• Hyperparameter tuning for federated learning – systems and practices
  crossref Syed Zawad, Feng Yan 2024-02-23T09:49:22Z 置信度 0.70
• Introduction to quantum federated machine learning
  crossref Samuel Yen-Chi Chen, Shinjae Yoo 2024-02-23T09:49:41Z 置信度 0.70
• Introduction to Federated Learning
  crossref Hamed Tabrizchi, Ali Aghasi 2025-04-23T14:07:49Z 置信度 0.70
• Introduction to Federated Learning
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Secure federated learning with Hindmarsh-Rose encryption
  crossref Jose L. Salmeron, Irina Arévalo 2026-06-12T10:49:31Z 置信度 0.70
• Mixed Federated Learning Algorithms
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Horizontal Federated Learning Algorithms
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Federated Learning Application Scenarios
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Federated Learning Based on Blockchain
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Vertically Federated Kernel Learning
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Key Variants of Federated Learning
  crossref Alexander Jung 2026-01-02T02:23:45Z 置信度 0.70
• Is the Future of AI Really Federated? Federated Learning as an Emerging Market
  crossref Bernd Beckert 2026-01-20T20:38:34Z 置信度 0.70
• Vertical Federated Deep Learning Algorithms
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Asynchronous Vertical Federated Learning Algorithm
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Digital healthcare systems in a federated learning perspective
  crossref Wasswa Shafik 2024-06-07T07:07:42Z 置信度 0.70
• Reliable federated learning for disease detection
  crossref Meng Wang, Huazhu Fu 2025-03-07T12:49:31Z 置信度 0.70
• Ethical considerations and legal issues relating to federated learning
  crossref Warren Chik, Florian Gamper 2024-02-23T09:49:53Z 置信度 0.70
• Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy
  crossref Turki Alhazmi, Farag Azzedin 2026-06-12T10:49:31Z 置信度 0.70
• Types of Federated Learning and Aggregation Techniques
  crossref S. Shailesh, Joseph James 2024-05-30T10:30:21Z 置信度 0.70
• Fundamentals of federated learning
  crossref Xiaoxiao Li, Ziyue Xu, Huazhu Fu 2025-03-07T12:49:13Z 置信度 0.70
• Algorithm 1 : Federated learning.
  crossref 2024-12-13T05:39:34Z 置信度 0.70
• Core Concepts of Federated Learning
  crossref Hamed Tabrizchi, Ali Aghasi 2025-04-23T18:07:49Z 置信度 0.70
• Scalability and Efficiency in Federated Learning
  crossref Alyan Zaib 2024-07-18T08:39:48Z 置信度 0.70
• Introduction of federated learning
  crossref Xiaoxiao Li, Ziyue Xu, Huazhu Fu 2025-03-07T12:49:11Z 置信度 0.70
• Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning
  crossref Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe 2021-06-11T03:42:05Z 置信度 0.70