# Data Poisoning Attacks to Local Differential Privacy Protocols

arXiv: https://arxiv.org/abs/1911.02046 · HTML: https://mlsanity.com/paper/1911.02046/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

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

- [Hidden Backdoors in Human-Centric Language Models](https://mlsanity.com/paper/2105.00164/) (2021)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning](https://mlsanity.com/paper/2205.06401/) (2022)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Regression with Label Differential Privacy](https://mlsanity.com/paper/2212.06074/) (2022)
- [Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy](https://mlsanity.com/paper/2202.10209/) (2022)
- [Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation](https://mlsanity.com/paper/2205.11782/) (2022)
- [Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning](https://mlsanity.com/paper/2208.09894/) (2022)
- [10 Security and Privacy Problems in Large Foundation Models](https://mlsanity.com/paper/2110.15444/) (2021)
- [Optimal Unbiased Randomizers for Regression with Label Differential Privacy](https://mlsanity.com/paper/2312.05659/) (2023)
- [SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning](https://mlsanity.com/paper/2205.09986/) (2022)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks](https://mlsanity.com/paper/2403.19510/) (2024)
- [PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy](https://mlsanity.com/paper/2110.11578/) (2021)
- [Robustness of Locally Differentially Private Graph Analysis Against Poisoning](https://mlsanity.com/paper/2210.14376/) (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)
- [Data Poisoning Attacks to Locally Differentially Private Range Query Protocols](https://mlsanity.com/paper/2503.03454/) (2025)
- [Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data](https://mlsanity.com/paper/2503.07483/) (2025)
- [Mitigating Data Poisoning Attacks to Local Differential Privacy](https://mlsanity.com/paper/2506.02156/) (2025)
- [Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols](https://mlsanity.com/paper/2506.09803/) (2025)
- [How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations](https://mlsanity.com/paper/2507.01487/) (2025)
- [Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach](https://mlsanity.com/paper/2403.10313/) (2024)
- [On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy](https://mlsanity.com/paper/2509.05265/) (2025)
- [dataRLsec: Safety, Security, and Reliability With Robust Offline Reinforcement Learning for DPAs](https://mlsanity.com/paper/2601.01289/) (2026)
- [Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning](https://mlsanity.com/paper/2606.17035/) (2026)
- [Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy](https://mlsanity.com/paper/2607.27815/) (2026)

## Abstract (arXiv)

Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs its data to preserve privacy before sending it to the data collector, who aggregates the perturbed data to obtain statistics of interest. In the past several years, researchers from multiple communities -- such as security, database, and theoretical computer science -- have proposed many LDP protocols. These studies mainly focused on improving the utility of the LDP protocols. However, the security of LDP protocols is largely unexplored. In this work, we aim to bridge this gap. We focus on LDP protocols for frequency estimation and heavy hitter identification, which are two basic data analytics tasks. Specifically, we show that an attacker can inject fake users into an LDP protocol and the fake users send carefully crafted data to the data collector such that the LDP protocol estimates high frequencies for arbitrary attacker-chosen items or identifies them as heavy hitters. We call our attacks data poisoning attacks. We theoretically and/or empirically show the effectiveness of our attacks. We also explore three countermeasures against our attacks. Our experimental results show that they can effectively defend against our attacks in some scenarios but have limited effectiveness in others, highlighting the needs for new defenses against our attacks.