Ensuring Synthetic Data Privacy

Anonymeter is a comprehensive tool to assess data anonymity and quickly evaluate privacy risks.

The Challenge of Synthetic Data Privacy Assessment
As synthetic data becomes more popular for innovation while complying with data privacy regulations, assessing re-identification risks is critical.

With Anonymeter, your data team can assess the robustness of synthetic data against re-identification attacks in a single click. Anonymeter simplifies the adoption of synthetic data and helps you comply with anonymization requirements.
Use Synthetic Data With Confidence
Anonymeter is the first tool to comprehensively evaluate the three key indicators of factual anonymization for synthetic data, allowing organizations to meet regulatory requirements.

Its results are easy-to-interpret, exhaustive, and robust, making it the perfect add-on for any organization.
Use Synthetic Data with Confidence
“The results produced by the tool Anonymeter should be used by the data controller to decide whether the residual risks of re-identification are acceptable or not, and whether the dataset could be considered anonymous. Anonymeter is a valuable tool, relevant in the context of personal data protection.”   CNIL Technology Experts Department

Comprehensive Synthetic Data Privacy Assessment
Comprehensive and Versatile Privacy Evaluations Anonymeter is the only framework that measures the singling out, linkability, and inference risks, the three criteria legally associated with re-identification risks, helping organizations meet the regulatory requirements of anonymized synthetic data.
Meets Regulatory Requirements CNIL recognizes that Anonymeter is aligned with the GDPR and is the first tool to introduce a comprehensive evaluation of the three key indicators of factual anonymization for synthetic data.
Interpretable and Easy-to-Use Anonymeter is easy to use and interpretable by privacy engineers and compliance teams. It requires only basic data analysis skills to interpret its results and is easily incorporated into synthetic data generation and governance pipelines.
Fast and Efficient Anonymeter simplifies synthetic data privacy evaluation, allowing organizations to reduce the time spent on developing compliant and robust privacy assessments. With Anonymeter, organizations can quickly identify and thus mitigate privacy risks, saving time and money.
Open Source and Modular Anonymeter is open source, enabling transparency and community contributions. The tool allows for integrating additional attack-based privacy metrics, making it an evolutive tool.
How it Works
Anonymeter evaluates the singling out, linkability, and inference risks associated with synthetic datasets. It then produces a report summarizing the privacy risks associated with the dataset and providing recommendations for mitigating them.

This risk-based approach is aligned with the GDPR Recital 26, which refers to all the means reasonably likely to be used to re-identify an individual in the anonymized dataset.
Linkability evaluator
Linkability evaluator
The Linkability evaluator measures the re-identification risk by evaluating how much help the synthetic data gives to an attacker who wants to establish links between records belonging to the same individual.
Inference evaluator
Inference evaluator
The Inference evaluator detects privacy leaks by assessing how much information an attacker with partial knowledge of the original data could gain by seeing the synthetic data. If the synthetic data provides more information about the training data than it provides about held-out test data, there is a privacy leak.
Singling Out evaluator
Singling Out evaluator
The Singling Out evaluator analyzes the probability of isolating records that would identify an individual. It measures the robustness of the dataset against a scenario in which an attacker would use the synthetic dataset to determine the presence of an individual in the dataset.
Privacy Enhancing Technologies Symposium
“Anonymeter has been presented in a peer-reviewed scientific paper and accepted at the 23rd Privacy Enhancing Technologies Symposium, which demonstrates its relevance and impact in the field of PETs.”
Can Anonymeter evaluate risks on pseudonymized or masked data?
Our goal is to create a comprehensive framework that can assess the privacy risks associated with the output of any data protection technique. Anonymeter can currently evaluate privacy risks on synthetic tabular data. However, our development team is working on expanding its capabilities to evaluate the risks of pseudonymized or de-identified data. This will provide organizations with a powerful tool for managing privacy risks across a wide range of data protection methods, ensuring they can protect sensitive data while maintaining compliance with privacy regulations.
Isn’t synthetic data private by default?
The synthetic data generation process irreversibly breaks one-to-one links between synthetic and real data records. This irreversible approach reduces the re-identification risk.

However, the deep learning models used for synthetic data generation might memorize features during the synthesization process. Ultimately, these memorized patterns can be reproduced in the synthetic data, leading to synthetic data privacy leaks.

The risk assessment of synthetic data is left up to each company's discretion. Due to the limited recommendations available, implementing a risk assessment becomes a challenge and puts individuals' privacy at risk. To address this gap, we developed a set of evaluations so you can measure the re-identification risks of synthetic data.
Where can I try Anonymeter?
Anonymeter is an open-source project that can be downloaded from our website or our GitHub repository. You also have access to Anonymeter if you currently use our products, the Data Embassy Platform, and the Data Embassy SDK.
What types of synthetic data does Anonymeter support?
Anonymeter supports tabular synthetic data.
Can Anonymeter be used for large-scale synthetic data evaluations?
Yes, Anonymeter is designed to be scalable and can be used for large-scale synthetic data evaluations. We have tested it on datasets of several million rows.
How long does it take to receive a privacy report from Anonymeter?
The time it takes to receive a privacy report from Anonymeter depends on the size and complexity of the evaluated synthetic data. However, most evaluations are completed within a few minutes.
Does Anonymeter only measure the re-identification risk of synthetic data?
Anonymeter version 1.0 measures the re-identification risks in synthetic tabular datasets. Anonymeter version 2.0, currently under development, also measures re-identification risks in Statutory Pseudonymized data.
Reduce the complexity, risk and time to realizing ROI for your data-driven initiatives. No other vendor in the data protection space offers a 100% accuracy guarantee compared to cleartext.
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