mlsanityData to 14 Oct 2026

Collecting and Analyzing Multidimensional Data with Local Differential Privacy

arXiv 1907.00782 ↗First posted 28 Jun 2019cs.CR cs.CY cs.DBcited by 49

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Abstract (arXiv)

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jun 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 13; 2023: 9; 2024: 2; 2025: 4; 2026: 12.

49 papers.

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  1. 2021Stronger Privacy for Federated Collaborative Filtering with Implicit Feedbackcited by 26
  2. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  3. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  4. 2022OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitizationcited by 12
  5. 2022XRand: Differentially Private Defense against Explanation-Guided Attackscited by 11
  6. 2022Residue-based Label Protection Mechanisms in Vertical Logistic Regressioncited by 10
  7. 2021FedXGBoost: Privacy-Preserving XGBoost for Federated Learningcited by 9
  8. 2022Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimationcited by 9
  9. 2021FLSys: Toward an Open Ecosystem for Federated Learning Mobile Appscited by 6
  10. 2022Nonparametric extensions of randomized response for private confidence setscited by 6
  11. 2023Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimationcited by 6
  12. 2021Task-aware Privacy Preservation for Multi-dimensional Datacited by 5
37 more
  1. 2023(Local) Differential Privacy has NO Disparate Impact on Fairnesscited by 5
  2. 2025FedGES: A Federated Learning Approach for BN Structure Learningcited by 4
  3. 2021Multi-Party Dual Learningcited by 3
  4. 2022FedPerm: Private and Robust Federated Learning by Parameter Permutationcited by 3
  5. 2023Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approachcited by 3
  6. 2023Privacy-Preserving Graph Embedding based on Local Differential Privacycited by 3
  7. 2022Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networkscited by 2
  8. 2023Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsificationcited by 2
  9. 2026How Much is Left? LLMs Linearly Encode Their Remaining Output Lengthcited by 2
  10. 2021WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacycited by 1
  11. 2021Locally Differentially Private Bayesian Inferencecited by 1
  12. 2022Locally Differentially Private Distributed Deep Learning via Knowledge Distillationcited by 1
  13. 2022One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitationcited by 1
  14. 2023Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statisticscited by 1
  15. 2023Shuffled Differentially Private Federated Learning for Time Series Data Analyticscited by 1
  16. 2023Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Webcited by 1
  17. 2024Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Datacited by 1
  18. 2024Beyond Statistical Estimation: Differentially Private Individual Computation via Shufflingcited by 1
  19. 2025Mitigating Data Poisoning Attacks to Local Differential Privacycited by 1
  20. 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
  21. 2025Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansioncited by 1
  22. 2026NOIR: Privacy-Preserving Generation of Code with Open-Source LLMscited by 1
  23. 2021Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learningcited by 0
  24. 2022Linear Model Against Malicious Adversaries with Local Differential Privacycited by 0
  25. 2022sqSGD: Locally Private and Communication Efficient Federated Learningcited by 0
  26. 2022BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcarecited by 0
  27. 2022Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Frameworkcited by 0
  28. 2026Classification Under Local Differential Privacy with Model Reversal and Model Averagingcited by 0
  29. 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregationcited by 0
  30. 2026Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Frameworkcited by 0
  31. 2026Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacycited by 0
  32. 2026Robust Estimation of Sparse Numerical Vectors under Local Differential Privacycited by 0
  33. 2026Private Direct Preference Optimization for LLM Alignmentcited by 0
  34. 2026ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledgecited by 0
  35. 2026Auditing Privacy Risks in LLM-Enhanced Graph Neural Networkscited by 0
  36. 2026Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocolscited by 0
  37. 2026HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systemscited by 0