PAPC: Platform Mediation for Privacy-Propagation Externalities in AI-Mediated Workflows
AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a final answer appears: a memory write, shared-workspace update, inter-agent message, or tool event may impose downstream exposure cost on another principal. We model this failure mode as a privacy-propagation externality, where the cost of a raw disclosure depends on topology and fanout as well as content. We present PAPC, a platform-mediated mechanism that intercepts information-moving events before they update shared state or external channels. PAPC combines policy, provenance, topology/fanout, privilege, and content signals to allow an event,...
| Authors | Tao Huang, Guosen Wu, Chen Hou et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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