# FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation

arXiv: https://arxiv.org/abs/2602.05838 · HTML: https://mlsanity.com/paper/2602.05838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data](https://mlsanity.com/paper/2201.12677/) (2022)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [Graphical-model based estimation and inference for differential privacy](https://mlsanity.com/paper/1901.09136/) (2019)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [Private Synthetic Data for Multitask Learning and Marginal Queries](https://mlsanity.com/paper/2209.07400/) (2022)
- [Systematic Assessment of Tabular Data Synthesis](https://mlsanity.com/paper/2402.06806/) (2024)
- [Benchmarking Differentially Private Tabular Data Synthesis](https://mlsanity.com/paper/2504.14061/) (2025)
- [Privacy Vulnerabilities in Marginals-based Synthetic Data](https://mlsanity.com/paper/2410.05506/) (2024)
- [FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://mlsanity.com/paper/2310.03447/) (2023)
- [CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources](https://mlsanity.com/paper/2402.08614/) (2024)
- [Construction of Differentially Private Summaries over Fully Homomorphic Encryption](https://mlsanity.com/paper/2112.08662/) (2021)

## Built on by (most cited first, 1 of 1)

- [RobPI: Robust Private Inference against Malicious Client](https://mlsanity.com/paper/2602.19918/) (2026)

## Abstract (arXiv)

Data is the lifeblood of AI, yet much of the most valuable data remains locked in silos due to privacy and regulations. As a result, AI remains heavily underutilized in many of the most important domains, including healthcare, education, and finance. Synthetic data generation (SDG), i.e. the generation of artificial data with a synthesizer trained on real data, offers an appealing solution to make data available while mitigating privacy concerns, however existing SDG-as-a-service workflow require data holders to trust providers with access to private data. We propose FHAIM, the first fully homomorphic encryption (FHE) framework for training a marginal-based synthetic data generator on encrypted tabular data. FHAIM adapts the widely used AIM algorithm to the FHE setting using novel FHE protocols, ensuring that the private data remains encrypted throughout and is released only with differential privacy guarantees. Our empirical analysis show that FHAIM preserves the performance of AIM while maintaining feasible runtimes.