mlsanityData to 7 Oct 2026

Local Differential Privacy and Its Applications: A Comprehensive Survey

arXiv 2008.03686 ↗First posted 9 Aug 2020cs.CRcited by 40

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Abstract (arXiv)

With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of users are growing concerned about their personal information, privacy preservation has become an urgent problem to be solved and has attracted significant attention. Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years. It breaks the shackles of the trusted third party, and allows users to perturb their data locally, thus providing much stronger privacy protection. This survey provides a comprehensive and structured overview of the local differential privacy technology. We summarise and analyze state-of-the-art research in LDP and compare a range of methods in the context of answering a variety of queries and training different machine learning models. We discuss the practical deployment of local differential privacy and explore its application in various domains. Furthermore, we point out several research gaps, and discuss promising future research directions.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 2; 2024: 15; 2025: 10; 2026: 4.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  3. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  4. 2024Differential Privacy Mechanisms in Neural Tangent Kernel Regressioncited by 14
  5. 2024Provable Differentially Private Computation of the Cross-Attention Mechanismcited by 14
  6. 2023LDP-Feat: Image Features with Local Differential Privacycited by 10
  7. 2024Incentives in Private Collaborative Machine Learningcited by 10
  8. 2021Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streamingcited by 8
  9. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  10. 2022On the Statistical Complexity of Estimation and Testing under Privacy Constraintscited by 6
  11. 2024Navigating the Landscape of Hint Generation Research: From the Past to the Futurecited by 6
  12. 2022Federated Calibration and Evaluation of Binary Classifierscited by 5
28 more
  1. 2023Measuring Re-identification Riskcited by 5
  2. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  3. 2022Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentationcited by 4
  4. 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffscited by 3
  5. 2024Learning with User-Level Local Differential Privacycited by 3
  6. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  7. 2024Machine unlearning through fine-grained model parameters perturbationcited by 2
  8. 2024Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacycited by 2
  9. 2024Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognitioncited by 2
  10. 2024Uncertainty quantification by block bootstrap for differentially private stochastic gradient descentcited by 1
  11. 2025Gaussian Differential Private Bootstrap by Subsamplingcited by 1
  12. 2025PASS: Private Attributes Protection with Stochastic Data Substitutioncited by 1
  13. 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
  14. 2025High-Probability Bounds For Heterogeneous Local Differential Privacycited by 1
  15. 2021HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generationcited by 0
  16. 2022Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learningcited by 0
  17. 2024Privacy-Preserving and Trustworthy Deep Learning for Medical Imagingcited by 0
  18. 2024A Survey on Differential Privacy for SpatioTemporal Data in Transportation Researchcited by 0
  19. 2024Testing Credibility of Public and Private Surveys through the Lens of Regressioncited by 0
  20. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  21. 2025Graph Privacy: A Heterogeneous Federated GNN for Trans-Border Financial Data Circulationcited by 0
  22. 2025Locally Differentially Private Thresholding Banditscited by 0
  23. 2025FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projectioncited by 0
  24. 2025Online Linear Regression with Paid Stochastic Featurescited by 0
  25. 2026Classification Under Local Differential Privacy with Model Reversal and Model Averagingcited by 0
  26. 2026ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregationcited by 0
  27. 2026Locally Private Parametric Methods for Change-Point Detectioncited by 0
  28. 2026Stabilizing black-box algorithms through task-oriented randomizationcited by 0