Zero-Trust Synthetic Data Generation with Verifiable Utility Guarantees

Liu et al. (2024) evaluate synthetic data quality but assume trusted generators, while Pappa et al. (2024) advocate zero-trust architectures. This research combines both: data owners collaboratively generate synthetic data via MPC (per Koch et al., 2021) and use verifiable computation to ensure utility (e.g., predictive accuracy) without accessing raw data. For example, hospitals could co-generate a synthetic TB dataset (per Orthi et al., 2025) where each party contributes encrypted statistical distributions, and an MPC protocol aggregates them into a synthetic dataset. Utility is verified via secure enclaves (per Widanage et al., 2021) that test model performance on the synthetic data. This challenges the norm of centralized synthetic data generation (per Liu et al., 2024) by enabling decentralized, auditable synthesis. The impact is a scalable solution for data markets with provable privacy-utility trade-offs.

References:

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