隐私计算与数据要素 · 全部实体（第 6 / 100 页）

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• A standardised differential privacy framework for epidemiological modelling with mobile phone data
  europepmc 2023 置信度 0.80
• Distribution-Invariant Differential Privacy.
  europepmc 2023 置信度 0.80
• Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility.
  europepmc 2023 置信度 0.80
• LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.
  europepmc 2024 置信度 0.80
• Impacts of census differential privacy for small-area disease mapping to monitor health inequities.
  europepmc 2023 置信度 0.80
• Hierarchical Aggregation for Numerical Data under Local Differential Privacy.
  europepmc 2023 置信度 0.80
• A federated learning differential privacy algorithm for non-Gaussian heterogeneous data.
  europepmc 2023 置信度 0.80
• A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy.
  europepmc 2023 置信度 0.80
• Task-Specific Adaptive Differential Privacy Method for Structured Data.
  europepmc 2023 置信度 0.80
• An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services.
  europepmc 2023 置信度 0.80
• Local differential privacy protection for wearable device data.
  europepmc 2022 置信度 0.80
• Heterogeneous Graph Neural Network with Semantic-aware Differential Privacy Guarantees
  europepmc 2023 置信度 0.80
• A Communication-Efficient, Privacy-Preserving Federated Learning Algorithm Based on Two-Stage Gradient Pruning and Differentiated Differential Privacy.
  europepmc 2023 置信度 0.80
• Differential privacy protection algorithm for network sensitive information based on singular value decomposition.
  europepmc 2023 置信度 0.80
• Differential privacy preserved federated transfer learning for multi-institutional ⁶⁸Ga-PET image artefact detection and disentanglement.
  europepmc 2023 置信度 0.80
• DPCL: Constrative Representation Learning with Differential Privacy
  europepmc 2022 置信度 0.80
• Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy.
  europepmc 2022 置信度 0.80
• Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.
  europepmc 2024 置信度 0.80
• Global and local impacts of differential privacy on estimates of health care inequity.
  europepmc 2022 置信度 0.80
• A federated learning differential privacy algorithm for non-Gaussian heterogeneous data
  europepmc 2022 置信度 0.80
• Federated <i>f</i>-Differential Privacy.
  europepmc 2021 置信度 0.80
• Differential privacy medical data publishing method based on attribute correlation.
  europepmc 2022 置信度 0.80
• DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy.
  europepmc 2023 置信度 0.80
• Differential Privacy Distributed Logistic Regression with Objective Function Perturbation
  europepmc 2022 置信度 0.80
• Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy.
  europepmc 2023 置信度 0.80
• <i>Sarve</i>: synthetic data and local differential privacy for private frequency estimation.
  europepmc 2022 置信度 0.80
• Exploring Intermediate Paradigms: A Comparative Analysis of Shuffle and Pan-Private Models in Differential Privacy with Emphasis on Trust Levels, Engineering, and Mathematical Perspectives
  europepmc 2023 置信度 0.80
• CONSISTENT SPECTRAL CLUSTERING OF NETWORK BLOCK MODELS UNDER LOCAL DIFFERENTIAL PRIVACY.
  europepmc 2022 置信度 0.80
• Personalized Sampling Graph Collection with Local Differential Privacy for Link Prediction
  europepmc 2022 置信度 0.80
• Restrictively Self-sampled and Compressed Local Differential Privacy in Federated Learning
  europepmc 2022 置信度 0.80
• Genomic Data Sharing under Dependent Local Differential Privacy.
  europepmc 2022 置信度 0.80
• Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range Query.
  europepmc 2022 置信度 0.80
• Differential privacy in health research: A scoping review.
  europepmc 2021 置信度 0.80
• Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs.
  europepmc 2023 置信度 0.80
• Differential privacy for eye tracking with temporal correlations.
  europepmc 2021 置信度 0.80
• Analysis of Application Examples of Differential Privacy in Deep Learning.
  europepmc 2021 置信度 0.80
• ABCDP: Approximate Bayesian Computation with Differential Privacy.
  europepmc 2021 置信度 0.80
• The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy.
  europepmc 2021 置信度 0.80
• The effect of differential privacy on Medicaid participation among racial and ethnic minority groups.
  europepmc 2022 置信度 0.80
• Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy.
  europepmc 2022 置信度 0.80
• A(DP) ²SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent With Differential Privacy.
  europepmc 2022 置信度 0.80
• An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy
  europepmc 2022 置信度 0.80
• A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.
  europepmc 2022 置信度 0.80
• Deep Learning with Gaussian Differential Privacy.
  europepmc 2020 置信度 0.80
• Differential Privacy and the Accuracy of County-Level Net Migration Estimates.
  europepmc 2022 置信度 0.80
• On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data.
  europepmc 2022 置信度 0.80
• B-DP: Dynamic Collection and Publishing of Continuous Check-In Data with Best-Effort Differential Privacy.
  europepmc 2022 置信度 0.80
• An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy.
  europepmc 2021 置信度 0.80
• Distributed Consensus Algorithms Based on Differential Privacy
  This paper investigates the design and analysis of distributed consensus algorithms incorporating differential privacy. Achieving consensus in a distributed system while simultaneously protecting the privacy of individual nodes' data presents a complex challen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Consensus Algorithms Based on Differential Privacy
  This paper investigates the design and analysis of distributed consensus algorithms incorporating differential privacy. Achieving consensus in a distributed system while simultaneously protecting the privacy of individual nodes' data presents a complex challen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Generative Modeling of Cryptographic Protocols with Differential Privacy
  This paper investigates the critical privacy concerns arising from training generative models of cryptographic protocols. Traditional generative modeling techniques, particularly Generative Adversarial Networks (GANs), can inadvertently leak sensitive informat…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Generative Modeling of Cryptographic Protocols with Differential Privacy
  This paper investigates the critical privacy concerns arising from training generative models of cryptographic protocols. Traditional generative modeling techniques, particularly Generative Adversarial Networks (GANs), can inadvertently leak sensitive informat…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Algorithmic Fairness via Differential Privacy and Representation Learning
  Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning systems. Traditional approaches to fairness often involve post-processing techniques or modifying the training data, which can negatively impact model …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Algorithmic Fairness via Differential Privacy and Representation Learning
  Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning systems. Traditional approaches to fairness often involve post-processing techniques or modifying the training data, which can negatively impact model …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy with Stochastic Gradient Descent via Optimal Transport
  This paper introduces a novel approach to achieving differential privacy within stochastic gradient descent (SGD) by leveraging the principles of optimal transport. Existing methods often struggle to balance privacy guarantees with model accuracy, frequently r…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy with Stochastic Gradient Descent via Optimal Transport
  This paper introduces a novel approach to achieving differential privacy within stochastic gradient descent (SGD) by leveraging the principles of optimal transport. Existing methods often struggle to balance privacy guarantees with model accuracy, frequently r…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum-Enhanced Differential Privacy for Graph Data
  This paper investigates the application of quantum error correction (QEC) to enhance differential privacy (DP) for graph data. Traditional DP mechanisms, when applied to graph datasets, often result in substantial utility loss due to the inherent structure and…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum-Enhanced Differential Privacy for Graph Data
  This paper investigates the application of quantum error correction (QEC) to enhance differential privacy (DP) for graph data. Traditional DP mechanisms, when applied to graph datasets, often result in substantial utility loss due to the inherent structure and…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Causal Discovery via Latent Variable Interventions with Differential Privacy
  This paper introduces a novel approach to causal discovery that integrates latent variable interventions with differential privacy. Existing causal discovery algorithms often suffer from biases introduced by observational data and lack adequate protection agai…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Causal Discovery via Latent Variable Interventions with Differential Privacy
  This paper introduces a novel approach to causal discovery that integrates latent variable interventions with differential privacy. Existing causal discovery algorithms often suffer from biases introduced by observational data and lack adequate protection agai…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Balancing the privacy-utility trade-off: How to draw reliable conclusions from private data
  Absolute anonymization, conceived as an irreversible transformation preventing re-identification and sensitive value disclosure, has proven to be a broken promise. Modern data protection must therefore shift toward a privacy-utility trade-off grounded in risk …
  datacite de Fondeville, Raphaël 2026 置信度 0.66 Methodology (stat.ME)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Performative Privacy: When Differential Privacy Maximizes Utility
  Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in the long term. However, this claim has not been formalized so far. In parallel, performative learning provide…
  datacite Mukherjee, Uddalak, Cyffers, Edwige, Chevaleyre, Yann 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Machine Learning (stat.ML)FOS: Computer and information sciences
• Personalized and Multi-View Representation for Federated Cold-Start Recommendation
  Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overlook the practical cold-item setting where new items continuously arrive. Under t…
  datacite Lim, Jaehyung, Kweon, Wonbin, Kim, Woojoo, Kim, Junyoung 等 2026 置信度 0.66 Information Retrieval (cs.IR)Machine Learning (cs.LG)FOS: Computer and information sciences
• Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spots
  Memorization in large language models is measured through a zoo of definitions whose formal relations are unknown, and differential privacy (DP) is treated as a proxy against all of them at once. We pin down the exact DP constant for the two that carry the pra…
  datacite Che, Xujun, Xu, Depeng, Yuan, Shuhan 2026 置信度 0.66 Cryptography and Security (cs.CR)Computation and Language (cs.CL)Machine Learning (cs.LG)FOS: Computer and information sciences
• Privacy-Aware Medical Image Analysis
  The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ult…
  datacite Lavish Kumar, Mohd Aamish, Murad Aalam, Himanshu Kumar Thakur 等 2026 置信度 0.66
• Privacy-Aware Medical Image Analysis
  The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ult…
  datacite Lavish Kumar, Mohd Aamish, Murad Aalam, Himanshu Kumar Thakur 等 2026 置信度 0.66
• Trustworthy Agentic AI: A Governance Framework
  Core Idea — Runtime Governance for Agentic AIThis preprint by Clive Aldred proposes a comprehensive governance framework that shifts trustworthy agentic AI from post-execution audits to real-time, cryptographically enforced compliance. It directly addresses th…
  datacite Aldred, Clive Gerald 2026 置信度 0.66
• Trustworthy Agentic AI: A Governance Framework
  Core Idea — Runtime Governance for Agentic AIThis preprint by Clive Aldred proposes a comprehensive governance framework that shifts trustworthy agentic AI from post-execution audits to real-time, cryptographically enforced compliance. It directly addresses th…
  datacite Aldred, Clive Gerald 2026 置信度 0.66
• NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification
  NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification Authors: NOUS_ONE Core Architecture Group Institutional Affiliation: Autonomous Systems & Quantum Assurance Laboratory Submission Date: 202…
  datacite Tully, Chloe 2026 置信度 0.66
• NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification
  NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification Authors: NOUS_ONE Core Architecture Group Institutional Affiliation: Autonomous Systems & Quantum Assurance Laboratory Submission Date: 202…
  datacite Tully, Chloe 2026 置信度 0.66
• AlgoExplorer: Una Libreria Python Interattiva per lo Studio degli Algoritmi
  # LUAlgoExplorer: A Semantic Hypergraph Engine and Universal Encyclopedia of Computational Algorithms **Author:** Luigi Usai **ORCID:** [0009-0003-3001-717X](https://orcid.org/0009-0003-3001-717X) **Software Version:** 2.0.0 **License:** GNU Affero General Pub…
  datacite Usai, Luigi 2026 置信度 0.66 AlgoritmiLista degli algoritmiSoftware per l'analisi degli algoritmi
• AlgoExplorer: Una Libreria Python Interattiva per lo Studio degli Algoritmi
  # LUAlgoExplorer: A Semantic Hypergraph Engine and Universal Encyclopedia of Computational Algorithms **Author:** Luigi Usai **ORCID:** [0009-0003-3001-717X](https://orcid.org/0009-0003-3001-717X) **Software Version:** 2.0.0 **License:** GNU Affero General Pub…
  datacite Usai, Luigi 2026 置信度 0.66 AlgoritmiLista degli algoritmiSoftware per l'analisi degli algoritmi
• Improving medical data synthesis with DP-GAN and Deep Anomaly Detection
  Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions,…
  datacite Crha, Vojtech, Hai, Rihan, Erkin, Zekeriya, Li, Tianyu 2024 置信度 0.66 Differential PrivacyAnomaly DetectionMedical Data Sharing
• Improving medical data synthesis with DP-GAN and Deep Anomaly Detection
  Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions,…
  datacite Crha, Vojtech, Hai, Rihan, Erkin, Zekeriya, Li, Tianyu 2024 置信度 0.66 Differential PrivacyAnomaly DetectionMedical Data Sharing
• Data Identification And Control Mechanism Using Distinguishing, Attacks In Cloud
  Cloud environment provides encrypted data management facility to the shared data. Security and privacy are guaranteed with encrypted storage support model. The data management and security requirements are handled by the cloud server only. Encrypted cloud stor…
  datacite Meena, S., Kowsalya, Dr. N. 2017 置信度 0.66 Outsourced Data SearchData CentersOrder Preserving Encryption and Differential Attacks
• Data Identification And Control Mechanism Using Distinguishing, Attacks In Cloud
  Cloud environment provides encrypted data management facility to the shared data. Security and privacy are guaranteed with encrypted storage support model. The data management and security requirements are handled by the cloud server only. Encrypted cloud stor…
  datacite Meena, S., Kowsalya, Dr. N. 2017 置信度 0.66 Outsourced Data SearchData CentersOrder Preserving Encryption and Differential Attacks
• The Invisible Patient: Bridging Personal Health Platforms with Hospital Care Through Extended Systems Theory, Professional Bureaucracy, and EU Interoperability Dimensions
  Analysis of the interoperability gap between personal health platforms and hospital care in Sweden. Uses the Five Model (extended with data, knowledge, context), Mintzberg's professional bureaucracy, EU Interoperability Framework, Ward and Daniel's Benefits Ma…
  datacite Waern, Nicolas 2026 置信度 0.66 interoperabilitypersonal health platformEU Interoperability FrameworkFive ModelMintzberg
• The Invisible Patient: Bridging Personal Health Platforms with Hospital Care Through Extended Systems Theory, Professional Bureaucracy, and EU Interoperability Dimensions
  Analysis of the interoperability gap between personal health platforms and hospital care in Sweden. Uses the Five Model (extended with data, knowledge, context), Mintzberg's professional bureaucracy, EU Interoperability Framework, Ward and Daniel's Benefits Ma…
  datacite Waern, Nicolas 2026 置信度 0.66 interoperabilitypersonal health platformEU Interoperability FrameworkFive ModelMintzberg
• DIFFERENTIAL PRIVACY-BASED PROTECTION OF USER SHOPPING PREFERENCES IN E-COMMERCE SYSTEMS
  This study investigates the potential of differentiated privacy to safeguard the purchase preferences of online consumers while simultaneously enabling data analysis and personalized services. Protecting user privacy is a challenging endeavor due to the extens…
  datacite Advanced Research and Development Journal 2026 置信度 0.66 Differential PrivacyE-Commerce SystemsUser Shopping PreferencesData PrivacyPrivacy Protection
• Large Language Models in the Acute Stroke Pathway: A Scoping Review of Applications, Evidence Maturity, and Implementation Readiness
  Background. Large language models (LLMs) have been rapidly adopted in medicine since late 2022, yet their role in the time-critical acute stroke pathway—from symptom recognition and prehospital triage to emergency diagnosis, imaging-related text tasks, reperfu…
  datacite Xianmu Luo, Hongsong Li, Xiaoli Liao 2026 置信度 0.66 Medicine and Health Sciencesclinical medicinescoping review
• Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist
  Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepa…
  datacite elrakhawi, mohamed kamal arafa 2026 置信度 0.66
• Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist
  Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepa…
  datacite elrakhawi, mohamed kamal arafa 2026 置信度 0.66
• Algorithmic Fairness through Differential Privacy and Fairness Constraints
  This paper investigates the challenging problem of achieving algorithmic fairness in machine learning systems while simultaneously protecting individual privacy. Traditional approaches to fairness often rely on post-processing techniques or modifying the learn…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Algorithmic Fairness through Differential Privacy and Fairness Constraints
  This paper investigates the challenging problem of achieving algorithmic fairness in machine learning systems while simultaneously protecting individual privacy. Traditional approaches to fairness often rely on post-processing techniques or modifying the learn…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Algorithmic Fairness Verification via Differential Privacy
  Algorithmic fairness is a critical concern in modern machine learning, yet achieving it remains a complex and often elusive goal. Traditional approaches to fairness verification are frequently hampered by ambiguity and a lack of formal guarantees. This paper p…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Algorithmic Fairness Verification via Differential Privacy
  Algorithmic fairness is a critical concern in modern machine learning, yet achieving it remains a complex and often elusive goal. Traditional approaches to fairness verification are frequently hampered by ambiguity and a lack of formal guarantees. This paper p…
  datacite Zhang, Jincheng 2026 置信度 0.66
• afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0
  First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network c…
  datacite afzalahmed786 2026 置信度 0.66
• afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0
  First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network c…
  datacite afzalahmed786 2026 置信度 0.66
• Differential Privacy via Randomized Graph Embeddings
  This paper introduces a novel approach to protecting individual privacy within graph data by leveraging randomized graph embeddings and differential privacy. The core challenge in applying differential privacy to graph data stems from the inherent interconnect…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy via Randomized Graph Embeddings
  This paper introduces a novel approach to protecting individual privacy within graph data by leveraging randomized graph embeddings and differential privacy. The core challenge in applying differential privacy to graph data stems from the inherent interconnect…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Generalized Differentiable Privacy for Quantum Computation Security Proof
  This paper presents a novel approach to achieving generalized differentiable privacy within the context of quantum computation security proofs. Current techniques for ensuring privacy in quantum computing often struggle to adequately address the unique challen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Generalized Differentiable Privacy for Quantum Computation Security Proof
  This paper presents a novel approach to achieving generalized differentiable privacy within the context of quantum computation security proofs. Current techniques for ensuring privacy in quantum computing often struggle to adequately address the unique challen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Differential Privacy via Distributed Hashing
  This paper presents a novel approach to achieving differential privacy in decentralized systems, utilizing distributed hashing techniques. The core concept revolves around obscuring individual data contributions within a global computation, thereby safeguardin…
  datacite Zhang, Jincheng 2026 置信度 0.66
• An Analysis of Differential Privacy Research in Location and Trajectory Data
  preprints 2020 置信度 0.74
• The Question of Privacy in the Quantum Era: A Systematic Literature Review on Privacy by Design, Differential Privacy, and Privacy Engineering Frameworks Using NLP Technqiues
  &lt;p dir="ltr"&gt;Quantum technology is set to revolutionize the way we interact with a variety of different industries- disrupting people, processes, and other technologies at an exponential rate. As of now, the majority of our trusted encryption methods, su…
  crossref Nour Mousa 2026-02-19T20:00:57Z 置信度 0.70
• Differential Privacy: A Primer
  Differential Privacy (DP) is a mathematical framework that protects individual privacy in data analysis while allowing useful insights to be extracted. It works by adding carefully calibrated noise to data or query results, ensuring that including or excluding…
  crossref Abhishek Tiwari 2025-12-06T21:02:59Z 置信度 0.70
• Figure 5: Architecture of proposed ViT with differential privacy.
  crossref 2026-08-21T15:47:15Z 置信度 0.70
• Have The Cake And Eat It Too: Differential Privacy Enables Privacy And Precise Analytics
  Abstract Existing research in differential privacy, whose applications have exploded across functional areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset and its utility for analytics. Resolving this trade-off critic…
  europepmc Rishabh Subramanian 2022 置信度 0.80
• Leveraging Differential Privacy for Targeted Advertisements
  Differential Privacy offers the online advertising industry new means to increase consumer privacy by obfuscating consumer data. While achieving the same privacy, these means decrease targeting accuracy differently, subsequently reducing advertisers’ willingne…
  crossref Lennart Kraft 2023-04-28T15:17:02Z 置信度 0.70
• Algorithms for synthetic data release under differential privacy
  crossref Jun Zhang 2019-10-02T11:05:04Z 置信度 0.70