Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
arXiv:2606.04067v1 Announce Type: cross Abstract: As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-essential sensitive disclosures, yet type based PII redaction is context agnostic and may raise two issues: over disclosing untyped sensitive context and over removing answer bearing spans. We recast privacy preserving query rewriting under Contextual Integrity: a span should be forwarded only if it is necessary for the task. We introduce DelegateCI-Bench, the first task based Contextual Integrity
As LLMs become increasingly integrated into enterprise and personal workflows, the practical challenges of sensitive data handling and privacy are becoming paramount, driving immediate research into solutions.
This research addresses a critical friction point for LLM adoption in regulated industries and for privacy-conscious users, enabling more secure and effective delegation of tasks to AI.
The proposed Contextual-Integrity-Grounded query rewriting shifts privacy protection from static type-based redaction to dynamic, task-necessity based filtering, improving both security and AI performance.
- · Enterprise LLM providers
- · Privacy-conscious organizations
- · Compliance software vendors
- · End-users of LLMs
- · LLM systems with poor privacy controls
- · Overly aggressive PII redaction methods
Enterprise adoption of LLMs accelerates as privacy concerns are systematically addressed.
New standards and protocols emerge for secure data handling in AI delegation across various sectors.
The development of 'private-by-design' AI agents becomes a competitive differentiator for cloud AI services.
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Read at arXiv cs.AI