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• PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy
  Since traditional federated learning (FL) algorithms cannot provide sufficient privacy guarantees, an increasing number of approaches apply local differential privacy (LDP) techniques to FL to provide strict privacy guarantees. However, the privacy budget heav…
  semanticscholar Baocang Wang, Yange Chen, Hang Jiang, Z. Zhao 2023-09-01 置信度 0.74 IEEE Internet of Things Journal
• PLDP-FL: Federated Learning with Personalized Local Differential Privacy
  As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated lear…
  europepmc Xiaoying Shen, Hang Jiang, Yange Chen, Baocang Wang 等 2023 置信度 0.80 Entropy
• Federated Quantum Machine Learning with Differential Privacy
  The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theore…
  semanticscholar Rod Rofougaran, Shinjae Yoo, H. Tseng, Samuel Yen-Chi Chen 2023-10-10 置信度 0.74 IEEE International Conference on Acoustics, Speech, and Signal Processing
• Toward Training at ImageNet Scale with Differential Privacy
  Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the training set. Despite a rich literature on how to train ML models with differentia…
  semanticscholar Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu 等 2022-01-28 置信度 0.74 arXiv.org
• Quantum Differential Privacy: An Information Theory Perspective
  Differential privacy has been an exceptionally successful concept when it comes to providing provable security guarantees for classical computations. More recently, the concept was generalized to quantum computations. While classical computations are essential…
  semanticscholar Christoph Hirche, C. Rouzé, Daniel Stilck França 2022-02-22 置信度 0.74 IEEE Transactions on Information Theory
• Between Privacy and Utility: On Differential Privacy in Theory and Practice
  Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be trans…
  semanticscholar Jeremy Seeman, Daniel Susser 2023-10-06 置信度 0.74 Social Science Research Network
• Differential Privacy Has Disparate Impact on Model Accuracy
  Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data. The cost of differential privacy is a reduction in the model's accuracy. We demonstrate that …
  semanticscholar Eugene Bagdasarian, Vitaly Shmatikov 2019-05-28 置信度 0.74 Neural Information Processing Systems
• Continual Observation under User-level Differential Privacy
  In the foundational work of Dwork et al. [15] on continual observation under differential privacy (DP), two privacy models have been proposed: event-level DP and user-level DP. The latter provides a much stronger notion of privacy, as it allows a user to contr…
  semanticscholar Wei Dong, Qiyao Luo, K. Yi 2023-05-01 置信度 0.74 IEEE Symposium on Security and Privacy
• Federated Learning With Sparsified Model Perturbation: Improving Accuracy Under Client-Level Differential Privacy
  Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm. …
  semanticscholar Rui Hu, Yanmin Gong, Yuanxiong Guo 2022-02-15 置信度 0.74 IEEE Transactions on Mobile Computing
• Combining homomorphic encryption and differential privacy in federated learning
  Recent works have investigated the relevance and practicality of using techniques such as Differential Privacy (DP) or Homomorphic Encryption (HE) to strengthen training data privacy in the context of Federated Learning protocols. As these two techniques cover…
  semanticscholar Arnaud Grivet Sébert, Marina Checri, O. Stan, Renaud Sirdey 等 2023-08-21 置信度 0.74 Conference on Privacy, Security and Trust
• Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
  Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factor…
  semanticscholar S. Denisov, H. B. McMahan, J. Rush, Adam D. Smith 等 2022-02-16 置信度 0.74 Neural Information Processing Systems
• The Discrete Gaussian for Differential Privacy
  A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite compu…
  semanticscholar C. Canonne, Gautam Kamath, T. Steinke, Clement Canonne 等 2020-03-31 置信度 0.74 Neural Information Processing Systems
• Federated learning and differential privacy for medical image analysis
  The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available mult…
  europepmc Mohammed Adnan, S. Kalra, Jesse C. Cresswell, Graham W. Taylor 等 2021-11-09 置信度 0.80 Scientific Reports
• Age-Dependent Differential Privacy
  The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact …
  semanticscholar Meng Zhang, Ermin Wei, R. Berry, Jianwei Huang 2022-06-06 置信度 0.74 IEEE Transactions on Information Theory
• Securing Approximate Homomorphic Encryption Using Differential Privacy
  semanticscholar Baiyu Li, D. Micciancio, Mark Schultz, Jessica Sorrell 2022 置信度 0.74 IACR Cryptology ePrint Archive
• Shuffled Model of Differential Privacy in Federated Learning
  semanticscholar Antonious M. Girgis, Deepesh Data, S. Diggavi, P. Kairouz 等 2021 置信度 0.74 International Conference on Artificial Intelligence and Statistics
• A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
  We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the ex ante privac…
  semanticscholar Alberto Blanco-Justicia, David Sánchez, J. Domingo-Ferrer, K. Muralidhar 2022-06-09 置信度 0.74 ACM Computing Surveys
• Local differential privacy and its applications: A comprehensive survey
  semanticscholar Mengmeng Yang, Taolin Guo, Tianqing Zhu, Ivan Tjuawinata 等 2023-12-01 置信度 0.74 Comput. Stand. Interfaces
• Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
  The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies …
  semanticscholar V. Feldman, Audra McMillan, Kunal Talwar 2022-08-09 置信度 0.74 ACM-SIAM Symposium on Discrete Algorithms
• LDP-IDS: Local Differential Privacy for Infinite Data Streams
  Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates this proble…
  semanticscholar Xuebin Ren, Liang Shi, Weiren Yu, Shusen Yang 等 2022-04-01 置信度 0.74 SIGMOD Conference
• A Customized Text Sanitization Mechanism with Differential Privacy
  As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based…
  semanticscholar Hui Chen, Fengran Mo, Yanhao Wang, Cen Chen 等 2022-07-04 置信度 0.74 Annual Meeting of the Association for Computational Linguistics
• One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy
  Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record’s membership in a dataset or even reconstruct this data record …
  semanticscholar Dayong Ye, Sheng Shen, Tianqing Zhu, B. Liu 等 2022-03-13 置信度 0.74 IEEE Transactions on Information Forensics and Security
• Guidelines for Evaluating Differential Privacy Guarantees
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  semanticscholar Naomi Lefkovitz 2023 置信度 0.74
• LDP-Fed: federated learning with local differential privacy
  This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical va…
  semanticscholar Stacey Truex, Ling Liu, Ka-Ho Chow, M. E. Gursoy 等 2020-04-27 置信度 0.74 EdgeSys@EuroSys
• Hyperparameter Tuning with Renyi Differential Privacy
  For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy lea…
  semanticscholar Nicolas Papernot, T. Steinke 2021-10-07 置信度 0.74 International Conference on Learning Representations
• An adaptive federated learning scheme with differential privacy preserving
  Abstract Driven by the upcoming development of the sixth-generation communication system (6G), the distributed machine learning schemes represented by federated learning has shown advantages in data efficient utilization and multi-party cooperative modeling. T…
  semanticscholar Xiang Wu, Yongting Zhang, Minyu Shi, Peichun Li 等 2021-09-15 置信度 0.74 Future generations computer systems
• Safe consensus control of cooperative-competitive multi-agent systems via differential privacy
  This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite con…
  semanticscholar Jiayue Ma, Jiangping Hu 2022-09-18 置信度 0.74 Kybernetika (Praha)
• Federated Learning with Local Differential Privacy: Trade-Offs Between Privacy, Utility, and Communication
  Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred…
  semanticscholar Muah Kim, O. Günlü, Rafael F. Schaefer 2021-02-09 置信度 0.74 IEEE International Conference on Acoustics, Speech, and Signal Processing
• Differential privacy in deep learning: Privacy and beyond
  semanticscholar Yanling Wang, Qian Wang, Lingchen Zhao, Congcong Wang 2023-11-01 置信度 0.74 Future generations computer systems
• Medical imaging deep learning with differential privacy
  The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical req…
  europepmc A. Ziller, Dmitrii Usynin, R. Braren, M. Makowski 等 2021 置信度 0.80 Scientific Reports
• Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
  Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL. To guarantee the client-level differential priv…
  semanticscholar Xinwei Zhang, Xiangyi Chen, Min-Fong Hong, Zhiwei Steven Wu 等 2021-06-25 置信度 0.74 International Conference on Machine Learning
• Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
  This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the c…
  semanticscholar Ferdinando Fioretto, Cuong Tran, P. V. Hentenryck, Keyu Zhu 2022-02-16 置信度 0.74 International Joint Conference on Artificial Intelligence
• Towards Private Learning on Decentralized Graphs With Local Differential Privacy
  Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect these local views of social graphs and conduct graph…
  semanticscholar Wanyu Lin, Baochun Li, Cong Wang 2022-01-23 置信度 0.74 IEEE Transactions on Information Forensics and Security
• Composition of Differential Privacy & Privacy Amplification by Subsampling
  This chapter is meant to be part of the book “Differential Privacy for Artificial Intelligence Applications.” We give an introduction to the most important property of differential privacy – composition: running multiple independent analyses on the data of a s…
  semanticscholar T. Steinke 2022-10-02 置信度 0.74 arXiv.org
• Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges
  The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some co…
  semanticscholar Bin Jiang, Jianqiang Li, Guanghui Yue, H. Song 2021-01-26 置信度 0.74 IEEE Internet of Things Journal
• Performance-Enhanced Federated Learning With Differential Privacy for Internet of Things
  Federated learning (FL), which enables multiple distributed devices (clients) to collaboratively train a global model without transmitting their private data, has attracted much attention in the Internet of Things (IoT) domain. Compared with centralized learni…
  semanticscholar Xicong Shen, Ying Liu, Zhaoyang Zhang 2022-12-01 置信度 0.74 IEEE Internet of Things Journal
• Collecting and Analyzing Multidimensional Data with Local Differential Privacy
  Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized versi…
  semanticscholar Ning Wang, Xiaokui Xiao, Y. Yang, Jun Zhao 等 2019-04-01 置信度 0.74 IEEE International Conference on Data Engineering
• Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
  What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \emph{private}? How much is the contribution of each specific training epoch to the information leakage through t…
  semanticscholar Rishav Chourasia, Jiayuan Ye, R. Shokri 2021-02-11 置信度 0.74 Neural Information Processing Systems
• "I need a better description": An Investigation Into User Expectations For Differential Privacy
  Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate …
  semanticscholar Rachel Cummings, Gabriel Kaptchuk, Elissa M. Redmiles 2021-10-13 置信度 0.74 Conference on Computer and Communications Security
• Rényi Differential Privacy of the Sampled Gaussian Mechanism
  The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications. The mechanism's unexpected power is derived from privacy amplification by sampling wh…
  semanticscholar Ilya Mironov, Kunal Talwar, Li Zhang 2019-08-28 置信度 0.74 arXiv.org
• Local Differential Privacy-Based Federated Learning for Internet of Things
  The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic informati…
  semanticscholar Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang 等 2020-04-01 置信度 0.74 IEEE Internet of Things Journal
• Debugging Differential Privacy: A Case Study for Privacy Auditing
  Differential Privacy can provide provable privacy guarantees for training data in machine learning. However, the presence of proofs does not preclude the presence of errors. Inspired by recent advances in auditing which have been used for estimating lower boun…
  semanticscholar Florian Tramèr, A. Terzis, T. Steinke, Shuang Song 等 2022-02-24 置信度 0.74 arXiv.org
• Mixed Differential Privacy in Computer Vision
  We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) gu…
  semanticscholar Aditya Golatkar, A. Achille, Yu-Xiang Wang, Aaron Roth 等 2022-03-22 置信度 0.74 Computer Vision and Pattern Recognition
• Fully Adaptive Composition in Differential Privacy
  Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameter…
  semanticscholar J. Whitehouse, Aaditya Ramdas, Ryan M. Rogers, Zhiwei Steven Wu 2022-03-10 置信度 0.74 International Conference on Machine Learning
• Dealer: An End-to-End Model Marketplace with Differential Privacy
  Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace p…
  semanticscholar Jinfei Liu, Jian Lou, Junxu Liu, Li Xiong 等 2021-02-01 置信度 0.74 Proceedings of the VLDB Endowment
• The Limits of Word Level Differential Privacy
  As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb wo…
  semanticscholar Justus Mattern, Benjamin Weggenmann, F. Kerschbaum 2022-05-02 置信度 0.74 NAACL-HLT
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
  Protecting large language models from privacy leakage is becoming increasingly crucial with their wide adoption in real-world products. Yet applying *differential privacy* (DP), a canonical notion with provable privacy guarantees for machine learning models, t…
  semanticscholar Weiyan Shi, Si Chen, Chiyuan Zhang, R. Jia 等 2022-04-15 置信度 0.74 Conference on Empirical Methods in Natural Language Processing
• Differential privacy and robust statistics in high dimensions
  We introduce a universal framework for characterizing the statistical efficiency of a statistical estimation problem with differential privacy guarantees. Our framework, which we call High-dimensional Propose-Test-Release (HPTR), builds upon three crucial comp…
  semanticscholar Xiyang Liu, Weihao Kong, Sewoong Oh 2021-11-12 置信度 0.74 Annual Conference Computational Learning Theory
• Instance-optimal Mean Estimation Under Differential Privacy
  Mean estimation under differential privacy is a fundamental problem, but worst-case optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensitivity is very large. Instead, various heuristics have been proposed to reduce th…
  semanticscholar Ziyue Huang, Yuting Liang, K. Yi 2021-06-01 置信度 0.74 Neural Information Processing Systems
• Antipodes of Label Differential Privacy: PATE and ALIBI
  We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples. We propose two novel approaches based on, respectively, the Laplace mechanism a…
  semanticscholar Mani Malek, Ilya Mironov, Karthik Prasad, I. Shilov 等 2021-06-07 置信度 0.74 Neural Information Processing Systems
• Projected Federated Averaging with Heterogeneous Differential Privacy
  Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important d…
  semanticscholar Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu 等 2021-12-01 置信度 0.74 Proceedings of the VLDB Endowment
• Federated Boosted Decision Trees with Differential Privacy
  There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models …
  semanticscholar Samuel Maddock, Graham Cormode, Tianhao Wang, C. Maple 等 2022-10-06 置信度 0.74 Conference on Computer and Communications Security
• Real-World Trajectory Sharing with Local Differential Privacy
  Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited…
  semanticscholar Teddy Cunningham, Graham Cormode, H. Ferhatosmanoğlu, D. Srivastava 2021-07-01 置信度 0.74 Proceedings of the VLDB Endowment
• Quantum machine learning with differential privacy
  Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of q…
  europepmc William Watkins, Samuel Yen-Chi Chen, Shinjae Yoo 2021-03-10 置信度 0.80 Scientific Reports
• Decentralized Wireless Federated Learning With Differential Privacy
  This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of…
  semanticscholar Shuzhen Chen, Dongxiao Yu, Yifei Zou, Jiguo Yu 等 2021-09-19 置信度 0.74 IEEE Transactions on Industrial Informatics
• Selective Differential Privacy for Language Modeling
  With the increasing applications of language models, it has become crucial to protect these models from leaking private information. Previous work has attempted to tackle this challenge by training RNN-based language models with differential privacy guarantees…
  semanticscholar Weiyan Shi, Aiqiang Cui, Evan Li, R. Jia 等 2021-08-30 置信度 0.74 North American Chapter of the Association for Computational Linguistics
• Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G
  Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) d…
  semanticscholar Jin Wang, Hui Han, Hao Li, Shiming He 等 2021-05-21 置信度 0.74 IEEE Transactions on Industrial Informatics
• Defending against Reconstruction Attacks with Rényi Differential Privacy
  Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, making this threat scenario an i…
  semanticscholar Pierre Stock, I. Shilov, Ilya Mironov, Alexandre Sablayrolles 2022-02-15 置信度 0.74 arXiv.org
• Consensus of Linear Multivariable Discrete-Time Multiagent Systems: Differential Privacy Perspective
  Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This ar…
  europepmc Yamin Wang, J. Lam, Hong Lin 2022 置信度 0.80 IEEE Transactions on Cybernetics
• Sequential Change Detection with Local Differential Privacy.
  europepmc 2026 置信度 0.80
• Identity trust management based on differential privacy for internet of vehicles.
  europepmc 2026 置信度 0.80
• Differential-Privacy-Based Collaborative Protection for Visual and Location Data in UAV Semantic Communications.
  europepmc 2026 置信度 0.80
• Differential Privacy Approaches in Telematics-Based Auto Insurance Systems
  europepmc 2026 置信度 0.80
• MobiShield: Federated Android Malware Detection with Adaptive Differential Privacy under Concept Drift
  europepmc 2026 置信度 0.80
• DP-DCGAN: Differential Privacy-Deep Convolutional Generative Adversarial Networks with Adaptive Gradient Perturbation
  europepmc 2026 置信度 0.80
• RiskSetDP: session-level differential privacy for survival analysis with risk-set-aware sensitivity control.
  europepmc 2026 置信度 0.80
• An AD Detection System Using Theme Enhancement and Dynamic Differential Privacy for Intelligent Elderly Care
  europepmc 2026 置信度 0.80
• Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.
  The growing adoption of artificial intelligence in healthcare highlights the need for models that can leverage heterogeneous patient data while preserving strict privacy requirements. This paper proposes a novel multi-modal federated learning framework with di…
  pubmed Hasan MR, Ahmed MI, Saha S, Ishika TK 等 2026 置信度 0.82
• Federated Learning with Differential Privacy for Ultrasound Breast Cancer Classification: An Empirical Study.
  Breast cancer is a critical global health challenge, and deep learning shows transformative potential for medical image classification. However, privacy regulations such as HIPAA and GDPR create barriers to centralized data aggregation across institutions. Thi…
  pubmed Makhanov N, Abdikenov B, Zhaksylyk T, Karibekov T 2026 置信度 0.82
• FedDriftGuard adaptive federated learning with differential privacy for concept drift in edge environments.
  Federated learning (FL) has become a highly promising paradigm for privacy-preserving distributed model training by enabling edge devices to train without sharing raw data. But in practice, edge environments are both non-stationary and asymmetric, with varying…
  pubmed Sudhakar K, Jayasree A, Sundaragiri D, Talluri U 等 2026 置信度 0.82
• Estimation of future occurrence of hemoglobin-A1c elevation with and without differential privacy.
  europepmc 2026 置信度 0.80
• Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation.
  Data privacy considerations and data fragmentation between healthcare institutions is causing segmentation of ischemic stroke lesions from neuroimaging to be hindered. The centralized approach may conflict with HIPAA and GDPR, and the federated learning approa…
  pubmed Adhi Siva M, Chowdhary CL 2026 置信度 0.82
• Integration of label-free electrochemical sensing, differential privacy deep learning, and blockchain for secure cytotoxicity monitoring.
  europepmc 2026 置信度 0.80
• FedAttn-Credit: attention-augmented federated learning with adaptive differential privacy for rural inclusive finance credit assessment.
  Rural inclusive finance faces persistent challenges in credit assessment due to fragmented data ecosystems, heterogeneous borrower profiles, and stringent privacy constraints. This paper proposes FedAttn-Credit, a multi-party collaborative credit assessment fr…
  pubmed Xie Z, Zhang L 2026 置信度 0.82
• Dynamic differential privacy technique for deep learning models.
  europepmc 2025 置信度 0.80
• Privacy-Preserving Anomaly Detection in Cloud Services Using Hierarchical Federated Learning with Differential Privacy
  europepmc 2026 置信度 0.80
• Step-Wise Dual Dynamic DPSGD: Enhancing Performance on Imbalanced Medical Datasets with Differential Privacy.
  europepmc 2026 置信度 0.80
• Splitting smarter: Differential privacy for secure healthcare federated learning.
  Split Federated Learning (SplitFed) has emerged as a decentralized method of training ML models that enables multiple healthcare parties to collaboratively share models without sharing their raw data. This method, however, is vulnerable to label inference atta…
  europepmc Munirat Yetunde Onireti, Raj Mani Shukla, Tapadhir Das 2025 置信度 0.80 InferenceComputer scienceMachine learningFederated learningVulnerability (computing)
• Assessing Local Differential Privacy for Compliance with the Personal Data Protection Law in Integrated Data Systems.
  europepmc 2026 置信度 0.80
• Classification Under Local Differential Privacy with Model Reversal and Model Averaging.
  europepmc 2026 置信度 0.80
• TrustFed-RHIO: an optimization-driven differential privacy federated learning framework for secure and explainable IIoT attack detection.
  A rapid proliferation of industrial internet of things (IIoT) systems has increased the vulnerability of interconnected devices for sophisticated cyberattacks, which necessitates intelligent and privacy-preserving solution for security. This paper presents Tru…
  pubmed Joseph L, Prabha B, Sambath M 2026 置信度 0.82
• Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.
  pubmed Mohammadi M, Vejdanihemmat M, Lotfinia M, Rusu M 等 2026 置信度 0.82
• Adaptive Compressed Sensing Differential Privacy Federated Learning Based on Orbital Spatiotemporal Characteristics in Space-Air-Ground Networks.
  europepmc 2026 置信度 0.80
• Integrating differential privacy into federated multi-task learning algorithms in &lt;b&gt;dsMTL&lt;/b&gt;.
  europepmc 2025 置信度 0.80
• An adaptive differential privacy framework for clinical llms with context-aware noise calibration, hierarchical budgeting, and real-time auditing.
  europepmc 2026 置信度 0.80
• Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy.
  Early diagnosis of thoracic diseases using chest x-ray imaging remains a critical challenge, particularly in resource-constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized s…
  pubmed Zulqarnain M, Hussain SJ, Aslam MZ, Fiaz A 等 2026 置信度 0.82
• Adaptive differential privacy mechanism for enhanced deep learning model utility and privacy.
  europepmc 2026 置信度 0.80
• Federated Learning for Privacy-Preserving Network Anomaly Detection: A High-Performance Convolutional Framework with Differential Privacy
  Abstract Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning, enabling multiple organizations to train shared models without exchanging sensitive data. This study presents a comprehensive investigat…
  europepmc Salah Eldin Olaymi 2026 置信度 0.80
• FedXGB-OptDP: A Privacy-Optimised Federated XGBoost Framework with Differential Privacy for IID and Non-IID healthcare data
  europepmc 2026 置信度 0.80
• Federated Learning with Differential Privacy for Sensitive Domains
  Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models across decentralized data sources while preserving data privacy. This approach is particularly beneficial in sensitive domains such as healthcare, finance, and tele…
  europepmc James Henderson, Racheal Writz 2025 置信度 0.80
• A Theoretically‐Grounded Federated Attribution Framework with Adaptive Differential Privacy Budgets for Cross‐Device Social Commerce Advertising Systems
  europepmc 2026 置信度 0.80
• Wasserstein GAN for moving differential privacy protection.
  europepmc 2025 置信度 0.80
• APB-FLDPA: Adaptive Personalized Blockchain-Federated Learning With Differential Privacy and Attention for Privacy-Preserving Healthcare Analytics.
  Developing robust medical artificial intelligence (AI) requires collaboration across multiple institutions, but strict data protection regulations such as HIPAA and GDPR prevent centralized patient data sharing. Existing federated learning (FL) methods often e…
  pubmed Chowdhury MKH, Mondal PK, Mozumder MAI, Kim HC 等 2026 置信度 0.82
• Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.
  europepmc 2026 置信度 0.80
• ALDP-FL for adaptive local differential privacy in federated learning.
  europepmc 2025 置信度 0.80
• Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis
  europepmc 2026 置信度 0.80
• Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy.
  europepmc 2025 置信度 0.80
• Impact of differential privacy on breast ultrasound image classification performance using vision transformer.
  europepmc 2025 置信度 0.80
• RUIP-BA: Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication via Random Projection and Local Differential Privacy for IoT and Mobile Platforms
  europepmc 2026 置信度 0.80
• Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems.
  europepmc 2025 置信度 0.80