arXiv:2605.20258v1 Announce Type: new Abstract: Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language models are increasingly deployed as personal agents handling sensitive workflows, adhering to CI becomes critical. However, even frontier models remain unreliable in making disclosure decisions, and existing mitigation strategies often degrade underlying task performance. To overcome this privacy-utility trade-off, we propose SELFCI, a complementary self-distillation fr
As large language models become personal agents, the tension between data utility and user privacy, especially concerning contextual integrity, reaches a critical point.
Ensuring LLMs can responsibly handle sensitive personal data without compromising core task performance is crucial for their broad adoption and trustworthiness in high-stakes applications.
The development of techniques like SELFCI indicates a path toward resolving the privacy-utility trade-off, potentially making LLMs more reliable for sensitive workflows.
- · AI developers
- · Users of LLM-powered agents
- · Privacy-focused tech companies
- · LLMs with poor privacy controls
- · Companies neglecting contextual integrity
Increased trust and adoption of AI agents for personal and sensitive tasks.
New privacy standards and regulations built around concepts like Contextual Integrity become more prevalent.
A competitive advantage for AI developers who successfully integrate robust privacy-preserving mechanisms into their models.
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