# A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis

arXiv: https://arxiv.org/abs/2010.05253 · HTML: https://mlsanity.com/paper/2010.05253/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [When differential privacy meets NLP: The devil is in the detail](https://mlsanity.com/paper/2109.03175/) (2021)
- [Privacy Preserving Prompt Engineering: A Survey](https://mlsanity.com/paper/2404.06001/) (2024)
- [Differentially Private Natural Language Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2301.09112/) (2023)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming](https://mlsanity.com/paper/2104.01808/) (2021)
- [When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes](https://mlsanity.com/paper/2207.08336/) (2022)
- [Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free](https://mlsanity.com/paper/2305.09668/) (2023)
- [Mean Estimation Under Heterogeneous Privacy Demands](https://mlsanity.com/paper/2310.13137/) (2023)
- [Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach](https://mlsanity.com/paper/2309.08569/) (2023)
- [OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification](https://mlsanity.com/paper/2202.07165/) (2022)
- [Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2307.00268/) (2023)
- [Private Estimation when Data and Privacy Demands are Correlated](https://mlsanity.com/paper/2407.11274/) (2024)
- [Gaussian Differential Private Bootstrap by Subsampling](https://mlsanity.com/paper/2505.01197/) (2025)
- [STAMP: Selective Task-Aware Mechanism for Text Privacy](https://mlsanity.com/paper/2603.12237/) (2026)
- [Locally Differentially Private Bayesian Inference](https://mlsanity.com/paper/2110.14426/) (2021)
- [Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent](https://mlsanity.com/paper/2405.12553/) (2024)
- [PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning](https://mlsanity.com/paper/2503.13173/) (2025)
- [High-Probability Bounds For Heterogeneous Local Differential Privacy](https://mlsanity.com/paper/2510.11895/) (2025)
- [Learning to Censor by Noisy Sampling](https://mlsanity.com/paper/2203.12192/) (2022)
- [Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models](https://mlsanity.com/paper/2506.17292/) (2025)
- [Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing](https://mlsanity.com/paper/2604.05077/) (2026)
- [Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling](https://mlsanity.com/paper/2607.08867/) (2026)
- [ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge](https://mlsanity.com/paper/2608.05737/) (2026)
- [Empirical Auditing of Edge-Private Graph Generators](https://mlsanity.com/paper/2609.25155/) (2026)

## Abstract (arXiv)

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user's data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. What's more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.