arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular data, auditing privacy remains hard. In many cases, even humans cannot easily tell if a table is real or synthetic. In this paper, we propose a method based on LLM discrimination. We ask an LLM to classify each table sample as REAL or SYNTHETIC. We test two settings: C1 with table only, and C2 with table plus distributional metadata. We use LLaMA as an open model and Gemini as a reference model. In o
The proliferation of synthetic data generation necessitates more robust methods for auditing its authenticity and privacy implications, coinciding with advanced large language models becoming sophisticated enough for discriminatory tasks.
The ability to accurately distinguish between real and synthetic data has critical implications for privacy, data utility, and the trustworthiness of AI systems deployed in sensitive domains.
Traditional synthetic data evaluation methods are augmented or potentially surpassed by LLM-based discrimination, suggesting a new benchmark for synthetic data quality and auditing.
- · AI ethicists
- · Data privacy regulators
- · Organizations using synthetic data
- · Malicious actors using synthetic data
- · Poorly designed synthetic data generators
Improved detection of synthetically generated data, enhancing data governance and privacy.
Increased pressure on synthetic data providers to develop more advanced obfuscation techniques or demonstrably robust privacy guarantees.
Potential for an 'arms race' between synthetic data generation and detection, driving innovation in both fields and raising the bar for data authenticity.
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