# When the signal is in the noise: Exploiting Diffix's Sticky Noise

arXiv: https://arxiv.org/abs/1804.06752 · HTML: https://mlsanity.com/paper/1804.06752/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [When differential privacy meets NLP: The devil is in the detail](https://mlsanity.com/paper/2109.03175/) (2021)
- [A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data](https://mlsanity.com/paper/2301.10053/) (2023)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [Exploiting Fairness to Enhance Sensitive Attributes Reconstruction](https://mlsanity.com/paper/2209.01215/) (2022)
- [Probabilistic Dataset Reconstruction from Interpretable Models](https://mlsanity.com/paper/2308.15099/) (2023)
- [QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems](https://mlsanity.com/paper/2211.05249/) (2022)
- [A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data](https://mlsanity.com/paper/2406.18671/) (2024)
- [A Unified Framework for Adversary-Aware Differential Privacy Bounds](https://mlsanity.com/paper/2507.08158/) (2025)
- [Supervised Machine Learning with Plausible Deniability](https://mlsanity.com/paper/2106.04267/) (2021)
- [DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics](https://mlsanity.com/paper/2504.18497/) (2025)

## Abstract (arXiv)

Anonymized data is highly valuable to both businesses and researchers. A large body of research has however shown the strong limits of the de-identification release-and-forget model, where data is anonymized and shared. This has led to the development of privacy-preserving query-based systems. Based on the idea of "sticky noise", Diffix has been recently proposed as a novel query-based mechanism satisfying alone the EU Article~29 Working Party's definition of anonymization. According to its authors, Diffix adds less noise to answers than solutions based on differential privacy while allowing for an unlimited number of queries. This paper presents a new class of noise-exploitation attacks, exploiting the noise added by the system to infer private information about individuals in the dataset. Our first differential attack uses samples extracted from Diffix in a likelihood ratio test to discriminate between two probability distributions. We show that using this attack against a synthetic best-case dataset allows us to infer private information with 89.4% accuracy using only 5 attributes. Our second cloning attack uses dummy conditions that conditionally strongly affect the output of the query depending on the value of the private attribute. Using this attack on four real-world datasets, we show that we can infer private attributes of at least 93% of the users in the dataset with accuracy between 93.3% and 97.1%, issuing a median of 304 queries per user. We show how to optimize this attack, targeting 55.4% of the users and achieving 91.7% accuracy, using a maximum of only 32 queries per user. Our attacks demonstrate that adding data-dependent noise, as done by Diffix, is not sufficient to prevent inference of private attributes. We furthermore argue that Diffix alone fails to satisfy Art. 29 WP's definition of anonymization. [...]