A Provenance DAG is a Directed Acyclic Graph used to track the origin and history of data or assets. This data structure records the lineage and transformations of digital items, showing how they were created, modified, and transferred over time. Each node in the graph represents an event or state, and directed edges indicate causal dependencies, ensuring that no cycles exist. Provenance DAGs provide an immutable and verifiable record of an asset’s journey, crucial for transparency and authenticity in digital supply chains or intellectual property management.
Context
Provenance DAGs are gaining relevance in blockchain and distributed ledger technology, particularly for applications requiring robust tracking of digital assets and their histories, such as NFTs or supply chain management. News reports often highlight their utility in combating counterfeiting, verifying authenticity, and ensuring transparent data trails. The adoption of such structures is a key development in establishing trust and accountability within decentralized ecosystems.
This theory introduces a Deterministic Causal Structure (DCS) where the ledger is a policy-agnostic DAG, resolving the entanglement of correctness and ordering.
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