用全同态加密保护隐私，实现加密数据上训练合成数据生成器。
FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation
- 在加密数据上运行改进的AIM算法，全程不解密原始数据。
- 合成数据质量接近原始模型，且运行时间可接受。
- 适合医疗、金融等强隐私要求领域的数据共享场景。
数据是AI的命脉，但许多重要领域（如医疗、教育、金融）的高价值数据因隐私和法规限制而被封锁。合成数据生成（SDG）通过训练合成器生成人工数据，可在保护隐私的同时释放数据价值，但现有SDG服务需数据所有者信任第三方。本文提出FHAIM，首个基于全同态加密（FHE）的边际型合成数据生成框架，可直接在加密的表格数据上训练合成器。通过设计新型FHE协议，将广泛使用的AIM算法适配至FHE环境，确保数据全程加密，仅以差分隐私保障形式释放。实证分析表明，FHAIM在保持与原始AIM相当性能的同时，具备可行的运行效率。
原文摘要 · Abstract (English)
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.
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