Presenting Anonymeter: The Tool for Assessing Privacy Risks in Synthetic Datasets

In this article, we introduce Anonymeter, an open-source tool developed by Anonos for organizations to evaluate the privacy risks of their synthetic data.
The National Commission on Informatics and Liberty (CNIL), France's data protection authority, recently assessed Anonymeter's effectiveness in evaluating privacy risks in anonymized and pseudonymized data. The CNIL Technology Expert Commission noted that the data controller should use the results produced by Anonymeter to decide whether the residual risks of re-identification are acceptable and whether the dataset can be considered anonymous.
In this blog post, we will discuss:

  • The privacy and legal challenges of synthetic data generation
  • Anonymeter’s ability to quickly evaluate privacy risks
  • How to use Anonymeter and why we made it open source

The privacy and legal challenges of synthetic data

Synthetic data is a valuable resource for businesses looking to develop and enhance their data-driven applications while ensuring the privacy of their customers and sensitive data. Today, many enterprises utilize synthetic data as an effective anonymization method to unlock new data sharing opportunities and gain competitive edges in their data operations.

However, complying with the legal requirements for data anonymization is a major challenge for enterprises. Evaluating privacy risks in synthetic data and interpreting technical risk assessments is complex, costly and time-consuming for compliance teams. Organizations must navigate the complex facets of the concept of re-identification to evaluate the risks associated with synthetic data. Data science teams may not have the necessary expertise to carry out this assessment effectively. The scale and diversity of synthetic data types, on the one hand, and the complexity and abundance of privacy metrics, on the other, make risk assessments a difficult task.

Additionally, interpreting the results of a privacy risk assessment in the eyes of regulatory frameworks can also reveal challenges. It requires specialized knowledge and expertise in data anonymization techniques, statistical analysis, and privacy regulations. The GDPR’s anonymization requirements stipulate that all means likely to be used for re-identification must be taken into account, and re-identification of a data subject in the dataset must no longer be possible. Still, there is no clear methodology or proposed threshold for defining re-identification, making it difficult to determine whether anonymization requirements have been met.

Anonymeter helps enterprises meet these challenges. It provides an easy-to-use framework to assess privacy risks in synthetic datasets. Anonymeter is used in the final stage of a synthetic data pipeline, ensuring that synthetic datasets are correctly anonymized and protected. It produces a comprehensive report summarizing the privacy risks associated with a dataset and providing indications of how to mitigate them.

Assessing the privacy risks of synthetic data with Anonymeter

Anonymeter is a software tool that implements different privacy attacks against synthetic tabular datasets and uses these attacks to estimate the re-identification risk. It provides organizations with comprehensive evaluations of privacy risks and demonstrates compliance with the GDPR's requirements for anonymized synthetic datasets.

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 provides recommendations for mitigating those risks.
Privacy is a multi-faceted concept reflected in the availability of dozens of different privacy metrics.
Privacy is a multi-faceted concept reflected in the availability of dozens of different privacy metrics. However, for most of these metrics, it is unclear how they translate into privacy implications in practice and what concrete privacy risks exist for individual data records.

Anonymeter is an empirical attack-based evaluation. It measures privacy risks by looking at how easy it is to attack the records in the original sensitive dataset from the synthetic one. Such an attack-based evaluation makes it easy to evaluate the context underlying the privacy risk and its practical implications.

Another distinguishing characteristic of Anonymeter is that it can differentiate between synthetic data privacy and utility as not all the information contained in the synthetic data constitutes a privacy breach. By making this distinction, Anonymter measures privacy risks in a sensitive and meaningful way.
Finally, unlike other approaches that attempt to assess the privacy risks associated with synthetic data generation processes (such as membership inference attacks), Anonymeter evaluates the privacy risks of synthetic data by analyzing the output of the data generation process, the synthetic data itself. It provides, therefore, a more direct measurement of the risk.

Anonymeter is relevant for enterprises using synthetic data due to its comprehensive and scalable framework for assessing and mitigating privacy risks.

The tool is designed to be widely usable and to provide interpretable results, requiring minimal manual configuration and no expert knowledge besides basic data analysis skills. It also applies to a wide range of datasets, numerical and categorical data types. It is the first tool to comprehensively evaluate the three key indicators of factual anonymization for synthetic data, allowing organizations to meet regulatory requirements.
Anonymeter has been accepted for presentation at the 23rd Privacy Enhancing Technologies Symposium, a leading forum for researchers and practitioners to present new advances in privacy-enhancing technologies.This recognition highlights Anonymeter’s significance and impact in the field of PETs.

How to evaluate the privacy of your synthetic data using Anonymeter

Anonymeter evaluates privacy risks from an attacker whose task is to use the synthetic dataset to come up with a set of guesses of the form:

  • "there is only one person with attributes X, Y, and Z" (singling out)
  • "records A and B belong to the same person" (linkability)
  • "a person with attributes X and Y also have Z" (inference)
Starting from these attack models, Anonymeter derives a privacy risk in percentage. This process is three-fold:

Phase 1: The attack

In this phase, the attack uses synthetic data to formulate a set of guesses.

Phase 2: The evaluation

Anonymeter checks the attack guesses against the values found in the original dataset to measure the attacker’s success .It is important to note that high degree of success for an attacker doesn't always indicate privacy issues. This scenario is similar to the famous “smoking causes cancer1” situation. The attacker might learn this causal correlation from the synthetic dataset and use it to make many correct guesses. The question arises: how do we determine if the attacker is making too many correct guesses?

Phase 3: The risk estimation

To answer this question, Anonymeter tasks the adversary to repeat the same attack, this time on a control dataset made up of records that were not used to generate the synthetic data. Any information the attacker obtains about the control records from the synthetic dataset cannot be attributed to privacy breaches because, by design, no information from the control dataset is transferred to the synthetic dataset. Comparing the success rates of the attack against training records and against control records, a sensitive and meaningful measure of risk is derived.
Comparing the success rates of the attack against training records and against control records, a sensitive and meaningful measure of risk is derived.
This analysis framework has been transformed into a lean software package that provides three main classes, one for each privacy risk. The only configuration needed is to specify what the attacker already knows (auxiliary information) and what it tries to learn. For example, Anonymeter can be used to measure the inference risk for each attribute in the dataset. See the picture below. This granular risk assessment is useful for understanding where vulnerabilities are and informing on how to mitigate them.
Measured inference risk
To get started, head over to GitHub, clone the Anonymeter repository, and install the package.

Why we developed Anonymeter as an open-source tool and what’s to come

Anonymeter was developed as part of Anonos's ambition to democratize the adoption of privacy-enhancing technologies such as synthetic data and Statutory Pseudonymization. We aim to make privacy risk evaluations accessible to organizations, particularly those lacking the expertise or resources to carry out these assessments effectively. For these reasons, we have released Anonymeter as an open-source library.

We believe that by providing a comprehensive and user-friendly tool like Anonymeter, we can help organizations adopt compliant privacy-enhancing technologies, without compromising the privacy of the individuals.

We aim to make Anonymeter a comprehensive and versatile tool for assessing the privacy risks associated with de-identification and anonymization methods. Thus we are extending its capacity and scope to assess a broader range of re-identification risks and support more data protection methods. This includes exploring new privacy metrics, enhancing the current ones, and adding support for new data types and de-identification methods such as Statutory Pseudonymization.

We also want to ensure that it remains relevant and up-to-date with the latest privacy research and regulations developments. Therefore, we are following the improvement recommendations from the CNIL and collaborating with standardization and research groups.

If you are interested in playing with Anonymeter, check out the example notebook in the repository.
1. As explained on “Statistical Inference is Not a Privacy Violation”: “[...] consider the role of smoking in determining cancer risk. Statistical study of medical data has taught us that smoking causes cancer. Armed with this knowledge, if we are told that 40 year old Mr. S is a smoker, we can conclude that he has an elevated cancer risk. The statistical inference of elevated cancer risk—made before Mr. S was born—did not violate Mr. S’s privacy.”