隐私计算与数据要素 · 全部实体（第 91 / 100 页）

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• SENTINEL-Chain: a blockchain-integrated privacy-preserving framework for secure healthcare data publishing.
  Electronic health records (EHRs) are central to healthcare analytics, but their granularity increases re-identification risk when shared. Conventional privacy-preserving methods including k -anonymity, l -diversity, and differential privacy often protect confi…
  pubmed Segar N, Vijayan V 2026 置信度 0.82
• Mitigating request flooding attack in named data networking using federated learning.
  Named Data Networking (NDN) represents a paradigm shift toward content-centric architectures but remains critically vulnerable to Interest Flooding Attacks (IFAs), where malicious actors overwhelm router Pending Interest Tables with spurious requests, causing …
  pubmed Benmaidi ML, Lagraa N, Brik B, Jlali L 2026 Jun 24 置信度 0.82
• Differentially Private Distributed Algorithms for Aggregative Games Over Directed Graphs With Linear Convergence.
  This article studies privacy-preserving distributed Nash equilibrium (NE) seeking for aggregative games over directed graphs, where agents' cost functions contain sensitive information. A novel differentially private algorithm using decaying Laplace noise is d…
  pubmed Wang L, He W, Qian F 2026 Jun 24 置信度 0.82
• Determinants and configurational pathways of healthcare big data asset valuation:an analysis using the TOE framework and fsQCA.
  Healthcare big data represents a strategic national resource with substantial potential value. At the policy level, ongoing efforts have been made to promote the transformation of healthcare data resources into data assets. However, the realization of their va…
  pubmed Jiang R, Yuan Z, Tian S, Yang C 等 2026 Jun 23 置信度 0.82
• Transcriptomic identification of key genes influencing m7G modification in thyroid carcinoma and experimental validation.
  N7-methylguanosine (m7G) modification plays a critical role in RNA metabolism and is increasingly recognized for its implications in cancer biology. It can influence RNA stability, translation efficiency, and gene expression regulation. However, the specific r…
  pubmed Li B, Zhu L, Zhang J 2026 Jun 22 置信度 0.82
• Testing the interleaving effect without response bias: A forced-choice reevaluation of Kornell and Bjork (2008).
  In a highly influential study across a wide breadth of literature, Kornell and Bjork (Psychological Science, 19[6], 585-592, 2008) showed that learning is enhanced by presenting exemplars (paintings) of to-be-learned categories (artists) in an interleaved sequ…
  pubmed Donenfeld J, Kaldy Z, Blaser E 2026 Jun 22 置信度 0.82
• Evaluating multi-level membership inference risk in federated EEG learning.
  Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordin…
  pubmed Khanam T, Siuly S, Wang K, Whittaker F 等 2026 Jun 21 置信度 0.82
• FL-TWIN: a unified federated learning system for intrusion detection with digital twins modelling.
  The growth of networked environments has intensified the challenge of detecting distributed denial-of-service (DDoS) attacks, as centralized intrusion detection systems face scalability, privacy, and data heterogeneity limitations. This paper proposes a federa…
  pubmed Hamwi AA, Mittal M 2026 Jun 20 置信度 0.82
• GLANet: global and local anomaly network for distributed cyber threat detection using FL.
  This paper introduces GLANet, a novel FL-based framework designed for real-time, privacy-preserving anomaly detection in distributed network environments. GLANet is a FL-based framework that integrates a lightweight convolutional neural network (CNN) as a loca…
  pubmed Othman Aljahdali A 2026 Jun 19 置信度 0.82
• SecureTrust-FL: trust-aware privacy-preserving federated learning for network intrusion detection.
  The proliferation of distributed network environments and the Internet of Things (IoT) has increased the need for privacy-preserving intrusion detection systems capable of operating effectively under heterogeneous and non-independent and identically distribute…
  pubmed Alshammari NS, Mishra S, Rathi M, Goel N 等 2026 Jun 19 置信度 0.82
• 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
• Can public data openness enhance public health resource allocation efficiency? Evidence from the launch of Chinese government data platforms.
  Public health resource allocation efficiency constitutes a critical pillar for protecting public health and social welfare. Nevertheless, data silos and information asymmetry constrain the optimization of public health resource allocation.
  pubmed Li Z, Zhang Z, Chu Y 2026 置信度 0.82
• Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.
  Due to the rapid digitization of healthcare systems, there has been a huge collection of sensitive personal data of patients. Thus, secure, privacy-preserving, and efficient data management systems are required. Current distributed healthcare systems increasin…
  pubmed Bhardwaj T, Sumangali K 2026 置信度 0.82
• Integration of label-free electrochemical sensing, differential privacy deep learning, and blockchain for secure cytotoxicity monitoring.
  Here, we present a blockchain-enabled, privacy-preserving platform that integrates label-free differential pulse voltammetry (DPV) measurements with image-based cell viability references for rapid and real-time monitoring of doxorubicin (DOX) induced cytotoxic…
  pubmed Turkmen H, Sezer BB, Şen M 2026 Oct 置信度 0.82
• Differential predictive value of inflammation- and coagulation-based composite biomarkers for venous thromboembolism and mortality in patients with NSCLC receiving immune checkpoint inhibitors.
  Venous thromboembolism (VTE) is a clinically important complication in patients with non-small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). Although inflammation and coagulation abnormalities may contribute to both thrombosis and poo…
  pubmed Liu J, Wang L, Xia X, Chen S 等 2026 Jun 17 置信度 0.82
• Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.
  Differentiating fungal from chronic sinusitis remains a diagnostic challenge due to overlapping symptoms.
  pubmed Li S, Li R, Ma Q, Zheng C 等 2026 Jun 16 置信度 0.82
• Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.
  The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision …
  pubmed Shahsavari Y, Baseri Y, Hafid A, Dambri OA 等 2026 Jun 15 置信度 0.82
• Serous Maculopathy due to Aspecific Choroidopathy (SMACH): a case report with differential diagnosis based on multimodal imaging analysis.
  Serous Maculopathy Due to Aspecific Choroidopathy (SMACH) is a rare chorioretinal disease characterized by polymorphic, non-pigmented choroidal lesions, with or without subretinal fluid (SRF). Its clinical manifestations overlap with common conditions such as …
  pubmed Ren X, Yu X, Huang S, Qi X 等 2026 Jun 15 置信度 0.82
• CASCADENCE: a layered cascade defense mechanism for federated learning.
  This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multi-layered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model p…
  pubmed Hashmi SW, Shukla RM, Bhunia S 2026 Jun 13 置信度 0.82
• Remote medical system driven by medical big models: Dynamic defense model for network security threats.
  As telemedicine systems become increasingly interconnected and medical big models are more widely deployed, remote medical infrastructures face growing cybersecurity risks. Traditional static defense mechanisms rely on predefined rule libraries and delayed pat…
  pubmed Weng Z, Hu Y, Gu D, Wang Z 等 2026 置信度 0.82
• Longitudinal adherence trajectories measured by electronic dose monitoring and viral non-suppression in a South African HIV cohort study.
  Sustained adherence to antiretroviral therapy (ART) is essential for maintaining viral suppression among people with HIV (PWH), yet adherence fluctuates over time and is imperfectly captured by routine viral load monitoring in many settings where measurements …
  pubmed Ferraris CM, Rosen JG, McDuling C, Schiavoni NJF 等 2026 Jun 11 置信度 0.82
• 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 Jun 11 置信度 0.82
• Cluster-randomized trial of Homework, Organization, and Planning Skills program compared to treatment as usual/waitlist for youth ages 11-14: Study protocol for conceptual replication.
  Organization, time management, and planning (OTMP) difficulties are associated with academic underachievement. OTMP skills training programs are effective in reducing OTMP deficits and improving academic performance. A randomized controlled trial of Homework, …
  pubmed Nissley-Tsiopinis J, Fleming PF, Chan W, Langberg JM 等 2026 置信度 0.82
• GLP-1-based therapy and ICD-10-documented heart failure or respiratory failure events in non-diabetic adults with rheumatoid arthritis and obesity: a TriNetX federated cohort study.
  Glucagon-like peptide-1 (GLP-1)-based therapies have emerged as a major advance in cardiometabolic care; however, no prospective outcomes data exist for these agents in non-diabetic adults with rheumatoid arthritis (RA) and obesity. We examined whether GLP-1-b…
  pubmed Loizidis G, Summer R 2026 Jul 置信度 0.82
• Privacy-preserving federated learning for interpretable student at-risk prediction across schools.
  Early-warning systems for at-risk students increasingly rely on predictive models trained on sensitive educational records. However, centralized learning pipelines raise concerns about privacy, institutional data sovereignty, and auditability, particularly whe…
  pubmed Jodayree M, Ghafi AK, Atashafrouz M, Shafiabadi MH 2026 Jun 10 置信度 0.82
• Federated continual learning for privacy-preserving chest radiograph classification.
  Multi-site deep learning for chest radiographs runs into two problems that are usually treated separately: privacy regulations prevent hospitals from pooling raw images, and clinical workflows change over time in ways that cause sequential model updates to ove…
  pubmed Sinhal A, Sinhal A, Sinhal A 2026 Jun 10 置信度 0.82
• Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals.
  In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transp…
  pubmed Ezzeddine F, Giordano S, Ayoub O 2026 置信度 0.82
• Efficient Privacy-Budgeted Large Language Model for Sensitive Healthcare.
  Large language models (LLMs) are promising for healthcare question answering, yet adapting them with sensitive clinical data can induce unintended memorization and privacy leakage. We present MedDP, an efficient privacy-budgeted adaptation framework that deliv…
  pubmed Zhang C, Zheng L, Huang H, Su C 2026 Jun 9 置信度 0.82
• Willingness to Undergo Human Papillomavirus Testing Among Men Who Have Sex With Men in China Based on the Information-Motivation-Behavioral Skills Model: Online Cross-Sectional Study.
  Men who have sex with men (MSM) are at high risk of human papillomavirus (HPV) infection, and HPV testing can facilitate early detection and timely intervention. However, evidence on the willingness to undergo different HPV testing modalities among MSM remains…
  pubmed Liu X, Yang X, Lyu M, Dai Z 等 2026 Jun 8 置信度 0.82
• Data Privacy, Ownership, and Secondary Use of Clinical Data Generated by Continuous Glucose Monitors and Mobile Health Applications: A Review.
  The growing use of continuous glucose monitors (CGMs) and mobile health (mHealth) applications has changed how diabetes is managed, allowing real-time tracking of glycemic patterns and remote clinical decision-making. These technologies also generate large vol…
  pubmed Dario P 2026 Jun 7 置信度 0.82
• Contrastive adapter training and consensus knowledge distillation for multi-source-free domain adaptation in skin cancer diagnosis.
  Skin cancer diagnosis, particularly the differentiation of melanoma from benign nevi, is a vital yet challenging task due to the visual similarity between lesions. Although deep learning models such as convolutional neural networks (CNNs) and vision transforme…
  pubmed Abedi A, Wu QMJ, Zhang N, Pourpanah F 2026 Oct 置信度 0.82
• Qualitative Insights on Client and Provider Barriers and Facilitators to Using a Novel Online HIV Pre- and Post-Exposure Delivery Model in Kenya.
  Online delivery of HIV pre- and post-exposure prophylaxis (PrEP and PEP) could address persistent access barriers, yet implementation across Africa remains limited. The ePrEP Kenya Pilot (NCT05377138) integrated PrEP and PEP services into an existing e-pharmac…
  pubmed Okello P, Thuo N, Roche SD, Saravis AL 等 2026 Jun 置信度 0.82
• 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 Jun 4 置信度 0.82
• Pembrolizumab-Induced Eruptive Keratoacanthomas Managed With Intralesional Corticosteroids: A Case Report.
  Pembrolizumab, a programmed cell death protein 1 (PD-1) inhibitor, is approved for multiple malignancies, including renal cell carcinoma in combination with axitinib. While highly effective, PD-1 blockade can provoke immune-mediated cutaneous adverse events, i…
  pubmed Varghese K, Vetos D, Woods J, Tate J 等 2026 May 置信度 0.82
• Recent advances in defending the privacy attacks of large language models for healthcare applications: a concise review.
  Large language models (LLMs) are increasingly adopted across healthcare applications, including clinical decision support and medical documentation systems. However, their deployment in medical settings raises significant privacy and security concerns due to t…
  pubmed Aroua R, Kammoun I, Louati MA, Adjeroh DA 等 2026 置信度 0.82
• Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.
  Large Language Models (LLMs) are being widely adopted in different domains including education, healthcare, and finance. In healthcare domain, LLMs are used in disease diagnosis, abnormality classification, remedy suggestions etc. Multi-abnormality classificat…
  pubmed Bhattacharjee P, Tian F, Rubin GD, Lo JY 等 2026 置信度 0.82
• Beyond the clot: diagnostic yield, biomarker performance, and the landscape of alternative diagnoses in emergency CT pulmonary angiography.
  Computed Tomography Pulmonary Angiography (CTPA) is the gold standard for diagnosing acute Pulmonary Embolism (PE). However, overlapping symptoms and reliance on non-specific biomarkers often lead to over-testing. This study provides a comprehensive analysis o…
  pubmed Lionetti F, Castelli F, Scavone G, Tamburino G 等 2026 Aug 置信度 0.82
• PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.
  As genomic research has grown increasingly popular in recent years, dataset sharing has remained limited due to privacy concerns. This limitation hinders the reproducibility and validation of research outcomes, both of which are essential for identifying compu…
  pubmed Jiang Y, Ji T, Ayday E 2026 置信度 0.82
• Extracellular volume fraction derived from spectral CT for liver function reserve evaluation and complication prediction in clinically stable liver cirrhosis.
  To investigate the value of spectral CT-derived extracellular volume fraction (fECV) in assessing liver functional reserve and predicting cirrhosis-related complications in clinically stable cirrhotic patients.
  pubmed Zhang L, Sun Y, Zhang X, Zhang Z 等 2026 May 28 置信度 0.82
• Urban-rural disparities in HPV prevalence and genotype distribution: a large-scale cervical cancer screening study in Xi'an, China.
  Cervical cancer remains a major public health challenge in China, with persistent urban-rural disparities in screening coverage. The epidemiological landscape of HPV genotype distribution and screening outcomes across geographic settings remains incompletely c…
  pubmed Wu J, Yi J, Fang L, Lei S 等 2026 May 28 置信度 0.82
• IER3 as an RFX5 transcriptional co-activator promotes hepatocellular carcinoma progression via AKR1B10-mediated p53 transcriptional regulation.
  Hepatocellular carcinoma (HCC) exhibits profound molecular heterogeneity, which complicates prognosis and therapy. Identifying key molecular subtypes and their driving oncogenes is crucial for developing targeted strategies. This study aimed to delineate chemo…
  pubmed Chen X, Zhang Q, Zhang N 2026 May 28 置信度 0.82
• Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture.
  This paper proposes a Vehicle-Road-Cloud-Chain (VRCC) four-layer collaborative framework to address the issues of lack of interpretability, reputation evaluation failure, and architecture centralization vulnerability faced by federated learning in Internet of …
  pubmed Zi Y, Zhou Y 2026 May 28 置信度 0.82
• Privacy-preserving Online Federated Learning for Massive Infinite Streams.
  Online federated learning (OFL) is essential for privacy-preserving collaborative online analytics over decentralized streams. Different from batch-based FL, OFL faces new challenges including longitudinal privacy leakage, and accumulated utility loss and comm…
  pubmed Shi L, Ren X, Yang S, Zhao C 等 2026 May 27 置信度 0.82
• Differential Privacy Issues
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Chapter 2 Local Differential Privacy for Privacy-preserving Machine
  crossref Graham Cormode 2025-07-23T08:26:52Z 置信度 0.70
• Chapter 3 Composition of Differential Privacy & Privacy Amplification by Subsampling
  crossref Thomas Steinke 2025-07-23T08:26:52Z 置信度 0.70
• Privacy-Preserving Trajectory Data Publishing via Differential Privacy
  crossref Ishita Dwivedi 2018-04-03T19:41:09Z 置信度 0.70
• Privacy-Preserving Genomic Data Publishing via Differential Privacy
  crossref Tanya Khatri 2019-01-22T18:59:21Z 置信度 0.70
• Composition of Differential Privacy & Privacy Amplification by Subsampling
  crossref Thomas Steinke 2026-08-04T15:34:34Z 置信度 0.70
• fdp: f-Differential Privacy and Gaussian Differential Privacy
  crossref Louis Aslett 2025-11-05T02:15:12Z 置信度 0.70
• Differential Privacy
  crossref 2017-05-12T21:45:17Z 置信度 0.70
• Practical differential privacy
  crossref Georgios Kellaris 2015-12-10T21:19:54Z 置信度 0.70
• Exploring the Accuracy and Privacy Tradeoff in AI-Driven Healthcare Through Differential Privacy
  crossref Surabhi Nayak, Sara Nayak 2025-02-25T06:22:45Z 置信度 0.70
• Combining differential privacy and PIR for efficient strong location privacy
  crossref King Hong Fung 2018-10-30T23:31:46Z 置信度 0.70
• Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy
  crossref Manish Kumar 2020-01-08T16:29:37Z 置信度 0.70
• Differential Privacy: Toward a Better Tuning of the Privacy Budget (ε) Based on Risk
  crossref Mahboobeh Dorafshanian, Mohamed Mejri 2023-03-04T05:14:25Z 置信度 0.70
• Chapter 16 Differential Privacy, Public Policy, and the Law
  crossref Jeremy Seeman 2025-07-23T08:26:52Z 置信度 0.70
• Chapter 17 Relationships between Differential Privacy and Algorithmic Fairness
  crossref Rachel Cummings 2025-07-23T08:26:52Z 置信度 0.70
• Learning with Differential Privacy
  crossref Peng Ye 2026-03-12T23:00:33Z 置信度 0.70
• Privacy-aware operations management: design and analysis of algorithms with differential privacy
  crossref Du Chen 2026-05-11T07:44:09Z 置信度 0.70
• Differential Privacy meets Verifiable Computation: Achieving Strong Privacy and Integrity Guarantees
  crossref Georgia Tsaloli, Aikaterini Mitrokotsa 2019-08-14T11:09:18Z 置信度 0.70
• tidydp: Tidy Differential Privacy
  crossref Thomas Tarler 2025-11-28T02:15:12Z 置信度 0.70
• Cloud-side Collaborative Privacy Protection Based on Differential Privacy
  crossref Zhenjiang Zhang Zhenjiang Zhang, Xiaohua Liu Zhenjiang Zhang 2021-08-20T06:54:59Z 置信度 0.70
• FLiPD: Privacy-Preserving Federated Learning via Multi-Party Computation and Differential Privacy
  crossref Gowri Chandran, Melek Önen, Thomas Schneider 2026-07-20T08:33:23Z 置信度 0.70
• Distributed Differential Privacy
  crossref 2025-01-10T20:06:00Z 置信度 0.70
• Privacy- & Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy
  crossref Luis Vasto-Terrientes, Sergio Martínez, David Sánchez 2025-02-25T01:22:45Z 置信度 0.70
• Differential Privacy, Public Policy, and the Law
  crossref Jeremy Seeman 2026-08-04T15:34:34Z 置信度 0.70
• Statistical Inference and Differential Privacy
  crossref Jordan Awan, Ruobin Gong 2024-08-23T14:36:41Z 置信度 0.70
• Differential Privacy for Geometric Data
  crossref Yuting Liang 2025-09-18T23:02:25Z 置信度 0.70
• Chapter 12 Programming Frameworks for Differential Privacy
  crossref Marco Gaboardi, Michael Hay, Salil Vadhan 2025-07-23T08:26:52Z 置信度 0.70
• Efficient Aggregation under Differential Privacy
  crossref Juanru Fang 2025-07-29T08:01:53Z 置信度 0.70
• Query evaluation under differential privacy
  crossref Wei Dong 2023-08-01T22:12:45Z 置信度 0.70
• Range counting under differential privacy
  crossref Ziyue Huang 2022-08-09T02:12:31Z 置信度 0.70
• Relationships between Differential Privacy and Algorithmic Fairness
  crossref Rachel Cummings 2026-08-04T15:34:34Z 置信度 0.70
• Glossary
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Acknowledgments
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Notes
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Differential Privacy and Marketing Analytics
  crossref Gilian Ponte 2024-05-14T09:12:56Z 置信度 0.70
• Bibliography
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Index
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Federated Learning with Differential Privacy and an Untrusted Aggregator
  crossref Kunlong Liu, Trinabh Gupta 2024-03-01T13:44:53Z 置信度 0.70
• Chapter 10 Differential Privacy in Energy Systems
  crossref James Anderson, Fengyu Zhou, Steven H. Low 2025-07-23T08:26:52Z 置信度 0.70
• Exploring Verifiable and Privacy-Preserving Federated Learning through Differential Privacy and Cryptographic Protocols
  Exploration de l'apprentissage fédéré vérifiable et respectueux de la vie privée grâce à la confidentialité différentielle et aux protocoles cryptographiques L’apprentissage fédéré est apparu comme un paradigme distribué permettant à plusieurs participants d’e…
  crossref Rezak Aziz 2026-05-25T01:39:26Z 置信度 0.70
• Exploring Differential Privacy in Practice
  crossref Davi Hasuda, Juliana Bezerra 2021-04-30T14:09:11Z 置信度 0.70
• Privacy-Preserving Distributed Estimation via Laplacian and Gaussian Differential-Privacy Noise
  Abstract In this paper, a private distributed estimation algorithm is proposed. In this algorithm, a differential-privacy noise is added to the intermediate estimation to be exchanged among nodes. Two types of differential noise is regarded in the paper which …
  europepmc Hadi Zayyani 2024 置信度 0.80
• Differential Privacy
  crossref Cynthia Dwork 2025-01-10T20:06:00Z 置信度 0.70
• \protect$\varepsilon$-Differential Privacy
  crossref 2025-01-10T20:06:00Z 置信度 0.70
• Series Foreword
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Review of Popular Algorithms for Differential Privacy
  crossref Ninghui Li, Tianhao Wang 2024-08-23T14:36:41Z 置信度 0.70
• Chapter 9 Differential Privacy and Medical Data Analysis
  crossref Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg 2025-07-23T08:26:52Z 置信度 0.70
• Future Directions
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Programming Frameworks for Differential Privacy
  crossref Marco Gaboardi, Michael Hay, Salil Vadhan 2026-08-04T15:34:34Z 置信度 0.70
• Challenges and Solutions to Deploying Differential Privacy
  crossref Damien Desfontaines, Christine Task 2026-08-04T15:34:34Z 置信度 0.70
• Differential Privacy Implementations
  crossref Matthew Graham, Andrew Foote, Lee Tucker, Hubert Janicki 2024-08-23T14:36:41Z 置信度 0.70
• Enhancing the Practicality of Differential Privacy
  crossref Dajun Sun 2025-09-18T23:06:44Z 置信度 0.70
• Further Reading
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• [ Front Matter ]
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• A Primer on ε-Differential Privacy
  crossref Ninghui Li, Min Lyu, Dong Su, Weining Yang 2022-06-08T16:21:04Z 置信度 0.70
• Privacy-Preserving Federated Learning  With  Adaptive Local Differential Privacy Under Low Privacy Budget
  crossref Xiaoying Shen, Jin Guo, Baocang Wang, Yange Chen 2024-11-12T14:37:25Z 置信度 0.70
• Differential privacy in the shuffle model
  crossref Albert Cheu 2022-08-24T14:53:21Z 置信度 0.70