AIMing Higher: A Smarter Approach to Privacy-Preserving Synthetic Data

At Tumult Labs, we are constantly innovating in privacy-preserving data solutions. Synthetic data is a powerful tool that allows organizations to analyze data without compromising individual privacy. However, generating synthetic data that is both private and useful remains a challenge.

You can read the full paper here.

How AIM Enhances MST

Both AIM and MST follow the same basic structure—Select, Measure, Generate—but AIM introduces several innovations that make it more effective:

1. Iterative Selection of Marginals: In MST, all marginals are selected upfront, but AIM takes an iterative approach. It continuously selects marginals throughout the process, refining the synthetic data step-by-step. Marginals are low-dimensional summaries of data, capturing specific relationships between variables. For example, a marginal might show how income relates to age in a dataset. By iteratively selecting the most valuable marginals at each stage, AIM improves the quality of the synthetic data as the process unfolds.

2. Privacy Loss Budget Awareness: AIM also adapts how many marginals are selected based on the available privacy budget. This allows it to efficiently manage the trade-off between privacy and utility, ensuring that the privacy budget is allocated where it matters most.

3. Workload Awareness: AIM adapts to the specific queries that the user wants to perform, selecting marginals that are most relevant to the user’s analysis tasks. By focusing on the most important queries, AIM ensures that the synthetic data is more accurate for the specific analysis needs of the user.

Why AIM Stands Out

The iterative selection of marginals, combined with AIM’s ability to adjust based on the privacy budget, makes it a more flexible and accurate method for generating differentially private synthetic data. These features enable AIM to produce synthetic data that is well-suited for the specific analysis tasks users need to perform, while also providing strong privacy guarantees.

Practical Benefits

For organizations that handle sensitive data—whether in healthcare, finance, or government—AIM offers an improved method for generating synthetic data that protects privacy while preserving the value of the original data. By adapting to the workload and efficiently managing the privacy budget, AIM ensures that organizations can confidently use synthetic data for a wide range of applications.

At Tumult Labs, we are committed to developing solutions that help organizations unlock the full potential of their data, without sacrificing privacy.