arXiv:2606.24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing differential privacy require the insertion of specially crafted canary data during training, making them impractical for auditing already-trained models without costly retraining. Additionally, dataset inference, which audits whether a suspect dataset was used to train a model, is infeasible without access to a private non-member held-out dataset. Yet, such held-out datasets are often unavailable or difficult to
The increasing deployment and reliance on large language models (LLMs) across sensitive applications necessitates robust privacy auditing methods, pushing research towards practical solutions.
This research provides a novel approach to auditing LLM privacy and data integrity without costly retraining or unavailable datasets, which is crucial for ethical AI development, compliance, and trust in AI systems.
The development of 'natural identifiers' makes privacy and data audits of pre-trained LLMs more feasible and effective, enabling better accountability and potentially new regulatory frameworks for AI.
- · AI developers
- · Auditors and regulators
- · Organizations deploying LLMs
- · Malicious actors exploiting LLM privacy vulnerabilities
- · LLM developers with opaque or non-compliant models
More widespread and effective privacy auditing of LLMs becomes possible, leading to enhanced data security.
Increased consumer and regulatory trust in AI systems, potentially accelerating AI adoption in sensitive sectors.
New industry standards and compliance requirements emerge around LLM privacy and auditability, driving innovation in secure AI design.
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