On the public citation graph
Gaussian Differential Privacy
0 signed · 387 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/978-3-662-49096-9_7openalex-snapshot
The Complexity of Computing the Optimal Composition of Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-662-53641-4_24openalex-snapshot
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
inferredcites →https://doi.org/10.1080/01621459.2017.1389735openalex-snapshot
Minimax Optimal Procedures for Locally Private Estimation
inferredcites →https://doi.org/10.1109/focs.2010.12openalex-snapshot
Boosting and Differential Privacy
inferredcites →https://doi.org/10.1109/focs.2014.56openalex-snapshot
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
inferredcites →https://doi.org/10.1109/focs.2015.46openalex-snapshot
Robust Traceability from Trace Amounts
inferredcites →https://doi.org/10.1109/isit.2011.6033729openalex-snapshot
Shannon meets Blackwell and Le Cam: Channels, codes, and statistical experiments
inferredcites →https://doi.org/10.1109/sp.2008.33openalex-snapshot
Robust De-anonymization of Large Sparse Datasets
inferredcites →https://doi.org/10.1109/tit.2017.2685505openalex-snapshot
The Composition Theorem for Differential Privacy
inferredcites →https://doi.org/10.1137/15m1033587openalex-snapshot
Fingerprinting Codes and the Price of Approximate Differential Privacy
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3188745.3188946openalex-snapshot
Composable and versatile privacy via truncated CDP
inferredcites →https://doi.org/10.1145/3219819.3226070openalex-snapshot
The U.S. Census Bureau Adopts Differential Privacy
inferredcites →https://doi.org/10.1198/jasa.2009.tm08651openalex-snapshot
A Statistical Framework for Differential Privacy
inferredcites →https://doi.org/10.1371/journal.pgen.1000167openalex-snapshot
Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4729530openalex-extracted
Finding the Best Arm Privately in Unimodal Bandits
inferred← citeshttps://doi.org/10.1002/cpe.70334openalex-snapshot
A Local Differential Privacy Method With Layer‐Wise Importance Based on Fisher Information in Federated Recommendation Systems
inferred← citeshttps://doi.org/10.1002/cpe.70699openalex-snapshot
DP‐HNAL: Differentially Private Training With Hierarchical Noise‐Aware Loss
inferred← citeshttps://doi.org/10.1002/nem.2292openalex-snapshot
Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions
inferred← citeshttps://doi.org/10.1002/sam.70053openalex-snapshot
Robust and Differentially Private Principal Component Analysis
inferred← citeshttps://doi.org/10.1002/sam.70097openalex-snapshot
A Nonparametric Two‐Sample Test Using a Parametric Integral Probability Metric
inferred← citeshttps://doi.org/10.1002/spy2.70051openalex-snapshot
User Data Privacy Protection Based on Machine Learning and Encryption Algorithms
inferred← citeshttps://doi.org/10.1002/spy2.70161openalex-snapshot
A Privacy Protection Method for Power User Profiles That Integrates Improved Differential Privacy and Secret Sharing
inferred← citeshttps://doi.org/10.1002/sta4.658openalex-snapshot
Highly private large‐sample tests for contingency tables
inferred← citeshttps://doi.org/10.1002/sta4.70030openalex-snapshot
Communication‐Efficient Distributed Gradient Descent via Random Projection
inferred← citeshttps://doi.org/10.1002/sta4.70170openalex-snapshot
Differentially Private Distributed Inference Based on U‐Statistics for Massive Data
inferred← citeshttps://doi.org/10.1007/978-3-031-10467-1_40openalex-snapshot
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-Purpose Regularizer and Privacy Budget Retrieval and Recycling
inferred← citeshttps://doi.org/10.1007/978-3-031-68024-3_4openalex-snapshot
Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage
inferred← citeshttps://doi.org/10.1007/978-3-031-70365-2_11openalex-snapshot
Secure Aggregation Is Not Private Against Membership Inference Attacks
inferred← citeshttps://doi.org/10.1007/978-3-031-72787-0_4openalex-snapshot
Fair and Private CT Contrast Agent Detection
inferred← citeshttps://doi.org/10.1007/978-3-031-89327-8_4openalex-snapshot
The Convergence of AI/ML and Cybersecurity: Advancing Digital Forensic Techniques
inferred← citeshttps://doi.org/10.1007/978-3-032-01940-0_5openalex-snapshot
Data Privacy in Enterprise AI
inferred← citeshttps://doi.org/10.1007/978-3-032-03985-9_8openalex-snapshot
Differential Privacy and Homomorphic Encryption in Healthcare Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/978-3-032-06096-9_23openalex-snapshot
MCMC for Bayesian Estimation of Differential Privacy from Membership Inference Attacks
inferred← citeshttps://doi.org/10.1007/978-3-032-16089-8_2openalex-snapshot
Lost in the Averages: Reassessing Record-Specific Privacy Risk Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-032-22730-0_22openalex-snapshot
Composition Theorems for f-Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-3-032-23335-6_18openalex-snapshot
Resilient and Explainable AI for Reliable Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-27834-0_14openalex-snapshot
Composition of Differential Privacy Mechanisms in Iterative Machine Learning: A Comparative Study
inferred← citeshttps://doi.org/10.1007/978-3-032-37883-5_3openalex-snapshot
Hypothesis Testing for Linear Regression Under Differential Privacy via Binning-Aggregation
inferred← citeshttps://doi.org/10.1007/978-981-92-0375-8_36openalex-snapshot
Private Memory Under the Spotlight: Diagnosing and Defending User-Level Leakage in LLM-Augmented Systems
inferred← citeshttps://doi.org/10.1007/978-981-95-9846-5_21openalex-snapshot
PDP-RAG: Privacy-Preserving Retrieval-Augmented Generation Based on Personalized Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-981-99-8082-6_40openalex-snapshot
Encrypted-SNN: A Privacy-Preserving Method for Converting Artificial Neural Networks to Spiking Neural Networks
inferred← citeshttps://doi.org/10.1007/s00778-026-00981-9openalex-snapshot
ResidualPlanner+: a scalable matrix mechanism for marginals and beyond.
inferred← citeshttps://doi.org/10.1007/s10107-023-01953-5openalex-snapshot
Optimal algorithms for differentially private stochastic monotone variational inequalities and saddle-point problems
inferred← citeshttps://doi.org/10.1007/s10479-026-07199-6openalex-snapshot
Linear optimization with local differential privacy for resource sharing
inferred← citeshttps://doi.org/10.1007/s10586-025-05310-3openalex-snapshot
EPAD: empowering privacy-preserving anomaly detection through homomorphic encryption and machine learning
inferred← citeshttps://doi.org/10.1007/s10586-025-05778-zopenalex-snapshot
FedFCA: Federated Formal Concept Analysis Framework
inferred← citeshttps://doi.org/10.1007/s10619-021-07346-xopenalex-snapshot
Healthcare Cramér Generative Adversarial Network (HCGAN)
inferred← citeshttps://doi.org/10.1007/s10845-026-02805-7openalex-snapshot
A privacy-enhancing federated learning framework for cross-manufacturer LPBF powder bed defect identification
inferred← citeshttps://doi.org/10.1007/s10994-022-06216-6openalex-snapshot
On the robustness of randomized classifiers to adversarial examples
inferred← citeshttps://doi.org/10.1007/s10994-024-06620-0openalex-snapshot
Fedpower: privacy-preserving distributed eigenspace estimation
inferred← citeshttps://doi.org/10.1007/s10994-026-07018-wopenalex-snapshot
Federated Learning on Riemannian Manifolds with Differential Privacy
inferred← citeshttps://doi.org/10.1007/s11082-023-05691-yopenalex-snapshot
RETRACTED ARTICLE: Research on key technologies of data security and privacy protection in Internet of Things group intelligence
inferred← citeshttps://doi.org/10.1007/s11222-022-10129-8openalex-snapshot
Statistic selection and MCMC for differentially private Bayesian estimation
inferred← citeshttps://doi.org/10.1007/s11222-025-10614-wopenalex-snapshot
Distributed empirical likelihood inference with privacy guarantees
inferred← citeshttps://doi.org/10.1007/s11227-024-06642-4openalex-snapshot
Optimizing data privacy: an RFD-based approach to anonymization strategy selection
inferred← citeshttps://doi.org/10.1007/s12239-026-00501-2openalex-snapshot
A risk-aware personalized differential privacy approach for automotive data sharing
inferred← citeshttps://doi.org/10.1007/s43926-025-00236-zopenalex-snapshot
Privacy-preserving federated learning approach based on Hensel’s compression and differential privacy
inferred← citeshttps://doi.org/10.1016/b978-0-323-85713-0.00017-7openalex-snapshot
Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making
inferred← citeshttps://doi.org/10.1016/b978-0-44-319037-7.00009-0openalex-snapshot
Considerations on the theory of training models with differential privacy
inferred← citeshttps://doi.org/10.1016/b978-0-44-323641-9.00017-0openalex-snapshot
Differential privacy
inferred← citeshttps://doi.org/10.1016/b978-0-443-26629-4.00170-2openalex-snapshot
Data confidentiality, differential privacy, and statistical disclosure control
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105893openalex-snapshot
FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN
inferred← citeshttps://doi.org/10.1016/j.cie.2025.111122openalex-snapshot
Temporal transaction network anomaly detection for Industrial Internet of Things with federated graph neural networks
inferred← citeshttps://doi.org/10.1016/j.comnet.2026.112335openalex-snapshot
Differential privacy for data sharing: evolution and full-lifecycle applications in generative AI
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103571openalex-snapshot
HP-MIA: A novel membership inference attack scheme for high membership prediction precision
inferred← citeshttps://doi.org/10.1016/j.cosrev.2024.100717openalex-snapshot
A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions
inferred← citeshttps://doi.org/10.1016/j.csda.2024.108041openalex-snapshot
Minimax rates of convergence for sliced inverse regression with differential privacy
inferred← citeshttps://doi.org/10.1016/j.csda.2025.108174openalex-snapshot
Discretization: Privacy-preserving data publishing for causal discovery
inferred← citeshttps://doi.org/10.1016/j.csda.2026.108416openalex-snapshot
Privacy-preserving estimation of weighted average treatment effects via bootstrap
inferred← citeshttps://doi.org/10.1016/j.dib.2026.112905openalex-snapshot
The Social Connectedness Index: A large-scale dataset of social ties across geographic locations
inferred← citeshttps://doi.org/10.1016/j.elerap.2025.101476openalex-snapshot
DP-SGD-global-adapt-V2-S: Triad improvements of privacy, accuracy and fairness via step decay noise multiplier and step decay upper clipping threshold
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108862openalex-snapshot
Enhanced collaborative intrusion detection for industrial cyber-physical systems using permissioned blockchain and decentralized federated learning networks
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112514openalex-snapshot
Privacy-preserving customer churn prediction model in the context of telecommunication industry
inferred← citeshttps://doi.org/10.1016/j.future.2023.06.010openalex-snapshot
Differential privacy in deep learning: Privacy and beyond
inferred← citeshttps://doi.org/10.1016/j.future.2025.107741openalex-snapshot
A quantization-based technique for privacy preserving distributed learning
inferred← citeshttps://doi.org/10.1016/j.future.2026.108554openalex-snapshot
Research on differentially private federated learning based on dynamic gradient clipping and layer-wise perturbation mechanisms
inferred← citeshttps://doi.org/10.1016/j.ijar.2024.109242openalex-snapshot
General inferential limits under differential and Pufferfish privacy
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102796openalex-snapshot
FedFR-ADP: Adaptive differential privacy with feedback regulation for robust model performance in federated learning
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122939openalex-snapshot
Enhancing privacy-preserving data synthesis utility with two-stage diffusion models
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101670openalex-snapshot
Key–value data collection with local differential privacy for urban air quality monitoring in crowdsensing
inferred← citeshttps://doi.org/10.1016/j.ipm.2024.103720openalex-snapshot
Matrix factorization recommender based on adaptive Gaussian differential privacy for implicit feedback
inferred← citeshttps://doi.org/10.1016/j.iswa.2026.200694openalex-snapshot
DP-RNTraj: A differential privacy diffusion model-based trajectory generation method with road network constraints
inferred← citeshttps://doi.org/10.1016/j.jeconom.2022.05.004openalex-snapshot
Distribution-invariant differential privacy
inferred← citeshttps://doi.org/10.1016/j.jisa.2025.103986openalex-snapshot
Accuracy-aware differential privacy in federated learning of large transformer models
inferred← citeshttps://doi.org/10.1016/j.jnca.2023.103714openalex-snapshot
A systematic review of federated learning: Challenges, aggregation methods, and development tools
inferred← citeshttps://doi.org/10.1016/j.jspi.2024.106186openalex-snapshot
Distributed optimal subsampling for quantile regression with massive data
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128424openalex-snapshot
Differentially private and explainable boosting machine with enhanced utility
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128756openalex-snapshot
Auditing privacy budget of differentially private neural network models
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.132180openalex-snapshot
Normalized clipping: A privacy-enhanced method in differentially private GANs
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.132893openalex-snapshot
Quantifying observable privacy in differentially private generative models under black-box access
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.134556openalex-snapshot
A comprehensive survey of differential privacy: Taxonomy and analysis based on symmetry
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.135236openalex-snapshot
A differential private release method for time series data based on silhouette coefficients
inferred← citeshttps://doi.org/10.1016/j.neunet.2026.108668openalex-snapshot
Differentially private data augmentation via LLM generation with discriminative and distribution-aligned filtering
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2026.122175openalex-snapshot
Privacy-preserving visualization of brain functional connectivity
inferred← citeshttps://doi.org/10.1016/j.rineng.2026.111232openalex-snapshot
An improved semi-supervised clustering method with differential privacy technology for seismic attributes analysis problems
inferred← citeshttps://doi.org/10.1016/j.sigpro.2024.109742openalex-snapshot
Adaptive compressed learning boosts both efficiency and utility of differentially private federated learning
inferred← citeshttps://doi.org/10.1016/j.slads.2026.100021openalex-snapshot
Privacy-preserving reinforcement learning from human feedback via decoupled reward modeling
inferred← citeshttps://doi.org/10.1016/j.sysarc.2025.103582openalex-snapshot
A survey of optimization algorithms for differential privacy in Federated Learning
inferred← citeshttps://doi.org/10.1038/s41586-026-10688-0openalex-snapshot
Disparate privacy risks from medical AI
inferred← citeshttps://doi.org/10.1038/s41598-024-77428-0openalex-snapshot
Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining
inferred← citeshttps://doi.org/10.1038/s41598-025-12575-6openalex-snapshot
ALDP-FL for adaptive local differential privacy in federated learning
inferred← citeshttps://doi.org/10.1038/s41598-026-38537-0openalex-snapshot
Tighter privacy auditing of differentially private stochastic gradient descent in the hidden state threat model
inferred← citeshttps://doi.org/10.1038/s43856-024-00462-6openalex-snapshot
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
inferred← citeshttps://doi.org/10.1049/2024/5553292openalex-snapshot
A Novel Differentially Private Online Learning Algorithm for Group Lasso in Big Data
inferred← citeshttps://doi.org/10.1049/cit2.12187openalex-snapshot
A federated learning scheme meets dynamic differential privacy
inferred← citeshttps://doi.org/10.1073/pnas.2307304121openalex-snapshot
AI model disgorgement: Methods and choices
inferred← citeshttps://doi.org/10.1073/pnas.2500337122openalex-snapshot
The 2020 US Decennial Census is more private than you (might) think
inferred← citeshttps://doi.org/10.1080/01621459.2024.2427436openalex-snapshot
Simulation-Based, Finite-Sample Inference for Privatized Data
inferred← citeshttps://doi.org/10.1080/01621459.2025.2602833openalex-snapshot
Emerging Knowledge Trend in Statistical Research: A Content-Based Analysis Using Covariate-Assisted Dynamic Topic Model
inferred← citeshttps://doi.org/10.1080/01621459.2025.2604315openalex-snapshot
Differentially Private Sliced Inverse Regression in the Federated Paradigm
inferred← citeshttps://doi.org/10.1080/01621459.2026.2644613openalex-snapshot
Adapting to Noise Tails in Private Linear Regression
inferred← citeshttps://doi.org/10.1080/03610926.2024.2370915openalex-snapshot
Asymptotic in a class of network models with an increasing sub-Gamma degree sequence
inferred← citeshttps://doi.org/10.1080/14498596.2026.2714779openalex-snapshot
Privacy-preserving spatial keyword query with exclusion keywords and multi-preference support in road networks
inferred← citeshttps://doi.org/10.1080/24754269.2026.2679084openalex-snapshot
A survey on differential privacy methods for big data privacy protection
inferred← citeshttps://doi.org/10.1088/1742-6596/2319/1/012031openalex-snapshot
Semantic Segmentation of Traffic Landmarks Using Classical Computer Vision and U-Net Model
inferred← citeshttps://doi.org/10.1093/imaiai/iaad030openalex-snapshot
Private quantiles estimation in the presence of atoms
inferred← citeshttps://doi.org/10.1093/jrsssb/qkaf062openalex-snapshot
Additive-Effect Assisted Learning
inferred← citeshttps://doi.org/10.1093/jrsssb/qkag059openalex-snapshot
Model privacy: a unified framework for understanding model stealing attacks and defences
inferred← citeshttps://doi.org/10.1093/jrsssb/qkag120openalex-snapshot
Knockoff inference under privacy constraints
inferred← citeshttps://doi.org/10.1109/access.2021.3124020openalex-snapshot
Enhancing Differential Privacy for Federated Learning at Scale
inferred← citeshttps://doi.org/10.1109/access.2022.3151670openalex-snapshot
Differential Privacy for Deep and Federated Learning: A Survey
inferred← citeshttps://doi.org/10.1109/access.2023.3243473openalex-snapshot
DPBA-WGAN: A Vector-Valued Differential Private Bilateral Alternative Scheme on WGAN for Image Generation
inferred← citeshttps://doi.org/10.1109/access.2024.3523909openalex-snapshot
A Survey of Differential Privacy Techniques for Federated Learning
inferred← citeshttps://doi.org/10.1109/asns67347.2025.11346052openalex-snapshot
Balancing Privacy Protection and Data Utility: Big Data Publishing Technology Based on Differential Privacy
inferred← citeshttps://doi.org/10.1109/comst.2024.3519861openalex-snapshot
Characterization and Quantification of User Privacy: Key Challenges, Regulations, and Future Directions
inferred← citeshttps://doi.org/10.1109/cscwd68734.2026.11582134openalex-snapshot
KL-CROSS: Robust Federated Learning via KL Divergence and Cross-Validation
inferred← citeshttps://doi.org/10.1109/csf61375.2024.00004openalex-snapshot
Composition in Differential Privacy for General Granularity Notions
inferred← citeshttps://doi.org/10.1109/csf64896.2025.00020openalex-snapshot
Session Types for the Concurrent Composition of Interactive Differential Privacy
inferred← citeshttps://doi.org/10.1109/csf68417.2026.00032openalex-snapshot
Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
inferred← citeshttps://doi.org/10.1109/csf68417.2026.00039openalex-snapshot
Information Leakage Envelopes
inferred← citeshttps://doi.org/10.1109/dcabes52998.2021.00030openalex-snapshot
DP-Face: Privacy-Preserving Face Recognition Using Siamese Network
inferred← citeshttps://doi.org/10.1109/fcn60432.2023.10543856openalex-snapshot
Preserving Privacy in the Age of Wireless Evolution: Differential Privacy in 5G, Beyond 5G, and 6G
inferred← citeshttps://doi.org/10.1109/flics70075.2026.11621914openalex-snapshot
Recommend Locally, Diversify Globally: FedFlex for Live Federated Netflix Recommendations
inferred← citeshttps://doi.org/10.1109/fnwf58287.2023.10520497openalex-snapshot
Private and Secure Machine Learning in Wireless Mobile Communication
inferred← citeshttps://doi.org/10.1109/hdis60872.2023.10499595openalex-snapshot
A Stationary Random Process Based Privacy-Utility Tradeoff in Differential Privacy
inferred← citeshttps://doi.org/10.1109/ic-aida68291.2026.11564377openalex-snapshot
False Security: On the Vulnerability of Perturbation-Based Model Stealing Defenses to Adaptive Attacks
inferred← citeshttps://doi.org/10.1109/icaiic54071.2022.9722703openalex-snapshot
Privacy-preserving collaborative machine learning in biomedical applications
inferred← citeshttps://doi.org/10.1109/icassp48485.2024.10446115openalex-snapshot
Rényi Differential Privacy in the Shuffle Model: Enhanced Amplification Bounds
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10890461openalex-snapshot
Differentially Private Distribution Estimation Using Functional Approximation
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11460317openalex-snapshot
Towards Practical Differential Privacy for Diffusion-Based Dataset Distillation
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11464727openalex-snapshot
GEIA: Generative Enhancement Inversion Attack Targeting Machine Unlearning
inferred← citeshttps://doi.org/10.1109/icc59461.2026.11587492openalex-snapshot
Over-the-Air Federated Compressive Learning with Privacy-Amplified Learnable One-Bit Updates
inferred← citeshttps://doi.org/10.1109/icdcs63083.2025.00045openalex-snapshot
FedLTH: A Privacy-preserving Federated Learning Framework with Model Pruning on Edge Clients
inferred← citeshttps://doi.org/10.1109/icdm54844.2022.00074openalex-snapshot
Privacy-Preserving Split Learning via Patch Shuffling over Transformers
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00078openalex-snapshot
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
inferred← citeshttps://doi.org/10.1109/icdmw60847.2023.00034openalex-snapshot
A Joint Permute-and-Flip and Its Enhancement for Large-Scale Genomic Statistical Analysis
inferred← citeshttps://doi.org/10.1109/ichi57859.2023.00022openalex-snapshot
Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy
inferred← citeshttps://doi.org/10.1109/ichi64645.2025.00032openalex-snapshot
Privacy Preservation Embedding-Based Clustering for Population Stratification Using Large Language Models
inferred← citeshttps://doi.org/10.1109/icimia67127.2025.11200738openalex-snapshot
Privacy Preservation in Distributed Healthcare Systems: A Review of Differential Privacy, Homomorphic Encryption, and Hybrid Approaches
inferred← citeshttps://doi.org/10.1109/iciptm69057.2026.11465974openalex-snapshot
AI-Enhanced Differential Privacy: A Secure and Scalable Approach to Data Protection
inferred← citeshttps://doi.org/10.1109/icnc57223.2023.10074197openalex-snapshot
Hensel’s Compression-Based Dimensionality Reduction Approach for Privacy Protection in Federated Learning
inferred← citeshttps://doi.org/10.1109/icpads47876.2019.00150openalex-snapshot
PPGAN: Privacy-Preserving Generative Adversarial Network
inferred← citeshttps://doi.org/10.1109/icscc59169.2023.10335074openalex-snapshot
A Review on Privacy Preserving Secure Machine Learning
inferred← citeshttps://doi.org/10.1109/icses63445.2024.10762992openalex-snapshot
Enhancing Trajectory Data Privacy through Information Gain-Fused Semantic Generalization
inferred← citeshttps://doi.org/10.1109/icssas66150.2025.11080921openalex-snapshot
Enhancing Data Privacy in Edge-based Driver AI Monitoring Systems Through Adaptive Differential Privacy
inferred← citeshttps://doi.org/10.1109/isbi56570.2024.10635222openalex-snapshot
Privacy-Preserving Visualization of Brain Functional Network Connectivity
inferred← citeshttps://doi.org/10.1109/isit45174.2021.9517999openalex-snapshot
Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence
inferred← citeshttps://doi.org/10.1109/isit50566.2022.9834392openalex-snapshot
An Adaptive Composition Theorem for Maximal Leakage for Binary Inputs
inferred← citeshttps://doi.org/10.1109/isit50566.2022.9834438openalex-snapshot
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206716openalex-snapshot
Total Variation with Differential Privacy: Tighter Composition and Asymptotic Bounds
inferred← citeshttps://doi.org/10.1109/isit57864.2024.10619626openalex-snapshot
Differential-Privacy Capacity
inferred← citeshttps://doi.org/10.1109/isit62367.2026.11653633openalex-snapshot
Composition Theorems for Multiple Differential Privacy Constraints
inferred← citeshttps://doi.org/10.1109/isit62367.2026.11653869openalex-snapshot
Adaptive Composition Theorems: Degeneracy Conditions and Leakage Growth
inferred← citeshttps://doi.org/10.1109/isit62367.2026.11654100openalex-snapshot
On the Generalization Error of Differentially Private Algorithms via Typicality
inferred← citeshttps://doi.org/10.1109/isit63088.2025.11195526openalex-snapshot
Differential Confounding Privacy and Inverse Composition
inferred← citeshttps://doi.org/10.1109/isit63088.2025.11195662openalex-snapshot
On the Extremal Mechanisms for Local Differential Privacy & Binary Maximal Leakage
inferred← citeshttps://doi.org/10.1109/jiot.2025.3572365openalex-snapshot
A Differential Privacy Based Task Offloading Algorithm for Vehicular Edge Computing
inferred← citeshttps://doi.org/10.1109/jiot.2025.3600617openalex-snapshot
Securing Federated Learning in IoT: A Survey of Attacks, Defenses, and Frameworks
inferred← citeshttps://doi.org/10.1109/jiot.2026.3653434openalex-snapshot
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions
inferred← citeshttps://doi.org/10.1109/jiot.2026.3709379openalex-snapshot
PRIDE: Privacy Through Deniability
inferred← citeshttps://doi.org/10.1109/jsait.2021.3054692openalex-snapshot
Three Variants of Differential Privacy: Lossless Conversion and Applications
inferred← citeshttps://doi.org/10.1109/jsait.2024.3370699openalex-snapshot
Flow-Based Distributionally Robust Optimization
inferred← citeshttps://doi.org/10.1109/jsait.2024.3384083openalex-snapshot
Total Variation Meets Differential Privacy
inferred← citeshttps://doi.org/10.1109/jsen.2025.3613334openalex-snapshot
Privacy-Preserving Cross-Silo Federated Learning for Remaining Useful Life Estimation of Distribution Transformers
inferred← citeshttps://doi.org/10.1109/mbits.2021.3108124openalex-snapshot
A Survey on Statistical, Information, and Estimation—Theoretic Views on Privacy
inferred← citeshttps://doi.org/10.1109/mobilecloud66020.2025.00007openalex-snapshot
Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments
inferred← citeshttps://doi.org/10.1109/qrs-c65679.2025.00018openalex-snapshot
FL-DPLoRA: An Integrated and Efficient Privacy-Preserving Training Framework for Large Language Models in Privacy-Critical Applications
inferred← citeshttps://doi.org/10.1109/satml59370.2024.00011openalex-snapshot
Improved Differentially Private Regression via Gradient Boosting
inferred← citeshttps://doi.org/10.1109/satml59370.2024.00029openalex-snapshot
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00060openalex-snapshot
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00061openalex-snapshot
Auditing Differential Privacy Guarantees Using Density Estimation
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00031openalex-snapshot
SoK: The Hitchhiker’s Guide to Efficient, End-to-End, and Tight DP Auditing
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00033openalex-snapshot
Position: Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00034openalex-snapshot
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
inferred← citeshttps://doi.org/10.1109/scecs69812.2026.11565932openalex-snapshot
An Early Warning Model for Rockburst in Coal Mines Based on the DP-Fed-CMR-MLP-Prox Model
inferred← citeshttps://doi.org/10.1109/sp40001.2021.00069openalex-snapshot
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
inferred← citeshttps://doi.org/10.1109/sp61157.2025.00064openalex-snapshot
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach
inferred← citeshttps://doi.org/10.1109/sp63933.2026.00058openalex-snapshot
CBUE: Conclusion Based Utility Evaluation for Differentially Private Categorical Data
inferred← citeshttps://doi.org/10.1109/sp63933.2026.00157openalex-snapshot
Making Privacy Public: Toward a Differential Privacy Deployment Registry
inferred← citeshttps://doi.org/10.1109/swc57546.2023.10449297openalex-snapshot
Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders
inferred← citeshttps://doi.org/10.1109/tbdata.2022.3190835openalex-snapshot
Privacy-Preserving Aggregation in Federated Learning: A Survey
inferred← citeshttps://doi.org/10.1109/tbdata.2024.3362191openalex-snapshot
Decentralized Federated Learning: A Survey on Security and Privacy
inferred← citeshttps://doi.org/10.1109/tce.2024.3412588openalex-snapshot
Secure Smart Health Care Data Transfer in Consumer Electronics Using Split Learning-Based Binary Sequential Split Vanilla Network
inferred← citeshttps://doi.org/10.1109/tdsc.2022.3174887openalex-snapshot
Privacy-Preserving Collaborative Data Collection and Analysis with Many Missing Values
inferred← citeshttps://doi.org/10.1109/tdsc.2023.3253520openalex-snapshot
Differentially Private Non-Negative Consistent Release for Large-Scale Hierarchical Trees
inferred← citeshttps://doi.org/10.1109/tdsc.2024.3364060openalex-snapshot
A Federated Learning Framework Based on Differentially Private Continuous Data Release
inferred← citeshttps://doi.org/10.1109/tdsc.2026.3715844openalex-snapshot
Fortress: Multi-Level Secure and Efficient Distributed Learning
inferred← citeshttps://doi.org/10.1109/tetc.2023.3244174openalex-snapshot
Preserving Differential Privacy in Deep Learning Based on Feature Relevance Region Segmentation
inferred← citeshttps://doi.org/10.1109/tetci.2024.3515007openalex-snapshot
Heterogeneity-Aware Clustering and Intra-Cluster Uniform Data Sampling for Federated Learning
inferred← citeshttps://doi.org/10.1109/tetci.2026.3670926openalex-snapshot
Toward Better Privacy-Protected Federated Learning for Healthcare Services in Non-IID Scenarios
inferred← citeshttps://doi.org/10.1109/tifs.2022.3169911openalex-snapshot
A Differentially Private Framework for Deep Learning With Convexified Loss Functions
inferred← citeshttps://doi.org/10.1109/tifs.2022.3185770openalex-snapshot
Offset-Symmetric Gaussians for Differential Privacy
inferred← citeshttps://doi.org/10.1109/tifs.2023.3306159openalex-snapshot
Grafting Laplace and Gaussian Distributions: A New Noise Mechanism for Differential Privacy
inferred← citeshttps://doi.org/10.1109/tifs.2024.3477325openalex-snapshot
Boosting Accuracy of Differentially Private Continuous Data Release for Federated Learning
inferred← citeshttps://doi.org/10.1109/tifs.2025.3536277openalex-snapshot
Differential Privacy With Higher Utility by Exploiting Coordinate-Wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions
inferred← citeshttps://doi.org/10.1109/tifs.2025.3557755openalex-snapshot
DC-SGD: Differentially Private SGD With Dynamic Clipping Through Gradient Norm Distribution Estimation
inferred← citeshttps://doi.org/10.1109/tifs.2025.3615460openalex-snapshot
Enhancing Federated Learning With Differentially Private Continuous Data Release via k -Ary Trees
inferred← citeshttps://doi.org/10.1109/tifs.2025.3642579openalex-snapshot
An Unbiased and Robust Privacy-Preserving Fingerprinting Scheme for Relational Databases
inferred← citeshttps://doi.org/10.1109/tifs.2026.3661818openalex-snapshot
Sensitivity-Aware Auditing Service for Differentially Private Databases
inferred← citeshttps://doi.org/10.1109/tifs.2026.3671100openalex-snapshot
A Differentially Private Quadrature Amplitude Modulation Mechanism for Federated Analytics
inferred← citeshttps://doi.org/10.1109/tit.2021.3111480openalex-snapshot
Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data
inferred← citeshttps://doi.org/10.1109/tmc.2023.3333879openalex-snapshot
Efficient and Privacy-Preserving Feature Importance-Based Vertical Federated Learning
showing the first 200 of 367 · page through all 367
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.