Introducing SDB: A New Standard for Synthetic Data Evaluation from SYNTHAINA AI

We are proud to announce the publication of our latest research paper, "Synthetic Data Blueprint (SDB)", now available on arXiv! 📄

At SYNTHAINA AI, we know that generating synthetic data is only half the battle. The real challenge is trust: How do you prove your synthetic data is as good as the real thing?

In this paper, our research team introduces SDB, a modular Python-based framework designed to solve the fragmentation in current evaluation methods. Unlike traditional tools that rely on simple statistics, SDB introduces a "multi-view" approach:

✅ Statistical Fidelity: Automated detection of distribution and dependency alignment.

✅ Structural & Graph-Based Metrics: ensuring the topology and geometry of the data remain intact.

✅ Domain-Agnostic: Validated across Healthcare (UCI Diabetes), Finance (Adult Income), and Cybersecurity (InSDN) use cases.

This framework represents a significant step forward in making AI trustworthy and auditable, right here from Greece 🇬🇷.

Read the full paper here: 👉 https://arxiv.org/abs/2512.19718