mlsanityData to 14 Oct 2026

Local Differential Privacy for Deep Learning

arXiv 1908.02997 ↗First posted 8 Aug 2019cs.LG cs.CRcited by 46

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Abstract (arXiv)

The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%- 96%) with high model quality even under low privacy budgets (e.g. varepsilon=0.5).

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Aug 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 11; 2023: 5; 2024: 11; 2025: 9; 2026: 5.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56
  3. 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
  4. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  5. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  6. 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
  7. 2022Quantum Local Differential Privacy and Quantum Statistical Query Modelcited by 12
  8. 2022You Are What You Write: Preserving Privacy in the Era of Large Language Modelscited by 12
  9. 2022Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learningcited by 11
  10. 2022XRand: Differentially Private Defense against Explanation-Guided Attackscited by 11
  11. 2024Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraintscited by 10
  12. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
34 more
  1. 2021Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network Designcited by 6
  2. 2023Stochastic Unrolled Federated Learningcited by 6
  3. 2024Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Testscited by 6
  4. 2021Task-aware Privacy Preservation for Multi-dimensional Datacited by 5
  5. 2023Local Differential Privacy in Graph Neural Networks: a Reconstruction Approachcited by 5
  6. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  7. 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloudcited by 5
  8. 2025Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Modelscited by 5
  9. 2024HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computingcited by 3
  10. 2025Tensorization of neural networks for improved privacy and interpretabilitycited by 3
  11. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  12. 2022Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?cited by 2
  13. 2022A Survey on Differential Privacy with Machine Learning and Future Outlookcited by 2
  14. 2025SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Datacited by 2
  15. 2026STAMP: Selective Task-Aware Mechanism for Text Privacycited by 2
  16. 2022User-Entity Differential Privacy in Learning Natural Language Modelscited by 1
  17. 2023Locally Differentially Private Distributed Online Learning with Guaranteed Optimalitycited by 1
  18. 2024Private Knowledge Sharing in Distributed Learning: A Surveycited by 1
  19. 2024Protecting Privacy in Classifiers by Token Manipulationcited by 1
  20. 2025PASS: Private Attributes Protection with Stochastic Data Substitutioncited by 1
  21. 2026BlindU: Blind Machine Unlearning without Revealing Erasing Datacited by 1
  22. 2022Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breachescited by 0
  23. 2022Momentum Gradient Descent Federated Learning with Local Differential Privacycited by 0
  24. 2023Verification of Neural Networks Local Differential Classification Privacycited by 0
  25. 2024Convergent Differential Privacy Analysis for General Federated Learningcited by 0
  26. 2024Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networkscited by 0
  27. 2024Effectiveness of L2 Regularization in Privacy-Preserving Machine Learningcited by 0
  28. 2025Token-Level Privacy in Large Language Modelscited by 0
  29. 2025Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offscited by 0
  30. 2025Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Modelscited by 0
  31. 2025PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstructioncited by 0
  32. 2026BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacycited by 0
  33. 2026Private Vertical Federated Inference for Time-Seriescited by 0
  34. 2026Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logisticscited by 0