Provenance-Based Data Acceptance in Cross-Organizational Pipelines: Design, Implementation, and Evaluation of dPLaaX and provin

Abstract

Recording provenance and deciding whether data may be accepted are not the same operation. As generative artificial intelligence expands cross-organizational data production, more data reaches downstream processing before human review. This paper tests a conditional, existential proposition: structuring the flow as a data pipeline, with cryptographic provenance from processing boundaries as the verification input, can give each recipient an independent acceptance basis at delivery — attributable acceptance: signed claims bound to issuing keys, decisions bound to evidence, profile, and payload, content truth excluded. The construction is specified in dPLaaX and partially implemented in the provin reference runtime: an EvidenceView fixes the credential representations, transformation sequence, and snapshot digests actually used; a recipient profile renders a fail-closed decision before the sink's writer runs. Separately implemented Python and Go verifiers computed identical EvidenceView identifiers on shared vectors. In a three-organization experiment, the sink reached different decisions on the same evidence under its two local profiles; a tampered payload produced no sink record; semantically inappropriate but correctly signed data was accepted under the provenance-only profile. Stopping the issuer's identity infrastructure immediately stopped default-posture deliveries; cached deliveries continued on locally held evidence within a declared freshness window. Full-path cost is linear in chain depth, 123 microseconds per credential; a measured ceiling near 330 deliveries per second under modelled resolution latency is lifted by an opt-in cache. The proposition partially holds; a global reading of the evidence-selection rule is unachievable under the committed premises, with a snapshot-relative replacement proposed, unimplemented. This record contains the English preprint (provenance-based-data-acceptance-preprint.pdf) and its Japanese translation (provenance-based-data-acceptance-preprint.ja.pdf).