Zero-Knowledge Open Innovation: Compute-to-Data and Cryptographic Contests for High-Security Ecosystems

Prototype an open innovation model where participants submit models or code to secure enclaves; results are validated and ranked using zero-knowledge proofs without revealing inputs or sensitive outputs. Differential privacy is layered in to bound leakage, and blockchain-based attestations ensure auditability. This model challenges the assumption that open innovation requires data sharing by operationalizing “paradoxical openness” via cryptography. It aligns with AI’s role in open innovation while resolving data-ownership and confidentiality hurdles, leverages permissioned blockchain governance for traceability, and addresses compliance gaps in emerging data spaces. It is directly applicable to life sciences and defense sectors, enabling these regulated domains to source external innovation at scale without compromising secrecy or IP. The approach fits enterprise compute-to-data trends and EU data space compliance requirements, opening large, currently closed innovation reservoirs and creating measurable, auditable pathways for collaboration in historically excluded domains.

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