arXiv:2606.16952v1 Announce Type: cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets. However, generating high-utility synthetic data often carries the risk of memorizing and regurgitating private information from the training corpus. In this work, we present a customizable empirical auditing framework designed to detect and explain such data disclosures. Our framework introduces a mechanism to distinguish between "true disclosures"-where the system dire
The rapid adoption of generative AI and LLMs, coupled with increasing regulatory scrutiny on data privacy, makes auditing synthetic data for disclosures an immediate and critical concern.
Ensuring the privacy and integrity of synthetic data is paramount for its broader adoption, as trust in these systems underpins their utility as a privacy-preserving alternative.
The ability to systematically detect and explain data disclosures in synthetic data fundamentally changes how generative AI can be deployed responsibly and securely.
- · AI developers focused on privacy
- · Organizations handling sensitive data
- · Data privacy regulators
- · Generative AI models with poor disclosure controls
- · Organizations misusing synthetic data
Increased trust and wider adoption of synthetic data as a privacy-preserving technology.
Development of industry standards and best practices for synthetic data generation and auditing.
New legal and ethical frameworks specifically addressing synthetic data disclosures and liabilities.
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Read at arXiv cs.AI