PAPC: Platform Mediation for Privacy-Propagation Externalities in AI-Mediated Workflows

Abstract: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, release a policy-safe abstraction, quarantine raw content, block a transition, or narrow onward rights. The model explains why final-output control misses intermediate exposure costs and why high-fanout objects amplify propagation. Across retrieval-memory and multi-agent workflow benchmarks, PAPC preserves deterministic task completion and eliminates measured exact raw-value and external raw-value exposure. The results position event-level mediation as a platform-governance primitive for agent-mediated online work.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2609.19226 [cs.CR]

(or arXiv:2609.19226v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.19226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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