Why sMPC is the Bridge Between Blockchain and Traditional Health IT

Pharma sponsors need immutable, auditable trial data but cannot expose sensitive patient PHI on-chain. sMPC creates a cryptographically verifiable data pipeline without raw data exposure.

• Key Benefit: Enables regulatory-grade audit trails for FDA submissions via on-chain proofs.
• Key Benefit: Allows multi-party computation on trial results (e.g., statistical analysis) where no single entity sees the raw inputs.

Hospitals and payers cannot share patient records directly due to HIPAA, crippling longitudinal care and research. sMPC enables privacy-preserving record linkage.

• Key Benefit: Run patient matching algorithms (e.g., on hashed identifiers) across competing health systems without revealing the underlying roster.
• Key Benefit: Unlocks decentralized identity models like ION or verifiable credentials for portable medical history.

Healthcare data is a $30B+ asset class trapped in Epic and Cerner silos. sMPC enables programmatic, privacy-preserving data unions for RWE.

• Key Benefit: Researchers can query a federated dataset (e.g., "patients with condition X on drug Y") and receive an aggregate, anonymized result without direct data access.
• Key Benefit: Creates a clear revenue model for data custodians via micropayments, tracked and settled on-chain, without ever moving the raw data.

Claims processing is a $500B+ manual reconciliation hellscape between providers, payers, and PBMs. sMPC acts as a trust-minimized oracle for sensitive business logic.

• Key Benefit: Executes multi-party contract logic (e.g., "does this patient's genomic data qualify for this specialty drug?") without exposing the patient's genome to the payer.
• Key Benefit: Slashes administrative costs by providing a single cryptographic truth for all parties, reducing disputes and audit overhead.

Traditional health IT relies on point-to-point API connections and centralized credential vaults, creating a massive attack surface. A single compromised server can expose millions of patient records.

• Attack Surface: Thousands of exposed API endpoints per health system.
• Compliance Cost: Manual audits for each data-sharing partnership cost $100k+.
• Data Silos: Legacy protocols cannot natively support multi-institutional computation.

sMPC replaces brittle API gateways with a cryptographic compute layer. Data never leaves its source custody; instead, encrypted shards are computed on across nodes. This aligns with HIPAA's Security Rule by design.

• Zero-Trust Model: Eliminates the need for broad data access credentials.
• In-Place Computation: Enables analytics on EMR data from Mayo Clinic, Kaiser, and Johns Hopkins without moving it.
• Regulatory Primitive: Provides a verifiable audit trail for data usage, satisfying GDPR and CCPA.

sMPC is computationally intensive. Training a complex model on distributed health data could take 100x longer than on centralized data, stalling real-time applications. Furthermore, threshold key management introduces operational complexity rivaling the legacy systems it aims to replace.

• Latency Overhead: ~2-10 second latency for simple queries, unacceptable for ICU monitoring.
• Node Churn Risk: The departure of sMPC nodes (e.g., hospital servers going offline) can halt entire computations.
• Adoption Friction: Requires health IT vendors like Epic and Cerner to integrate deep cryptographic clients.

The pragmatic path combines sMPC with Zero-Knowledge Proofs (ZKPs) and selective on-chain settlement. Use sMPC for private computation, generate a ZKP of the result's validity, and post only the proof to a blockchain like Ethereum or Avail for audit and payment. This mirrors the UniswapX model for intents.

• Layer Separation: sMPC for privacy, ZKP for verification, Blockchain for finality.
• Cost Optimization: On-chain footprint reduced by >99%, making micro-payments feasible.
• Progressive Decentralization: Start with a permissioned sMPC network, evolve to a permissionless one.

FHE allows computation on encrypted data without sMPC's multi-party setup, a cleaner architectural fit. However, its performance is still prohibitive for large-scale health datasets. Projects like Fhenix and Inco are betting on FHE coprocessors, but healthcare cannot wait for hardware breakthroughs.

• Performance Gap: FHE is ~1000x slower than plaintext computation today.
• Single Point of Trust: Data is encrypted to a single public key, concentrating risk.
• Immature Ecosystem: Lacks the battle-tested libraries and protocols of sMPC.

sMPC is the only viable on-ramp for trillion-dollar health IT systems onto decentralized infrastructure. It provides the necessary cryptographic wrapper for legacy data. The end-state is a hybrid network where sMPC handles sensitive computation, ZKPs provide verification, and blockchains orchestrate trust—akin to Across Protocol for cross-chain liquidity but for health data.

• Strategic Bridge: Enables $4T healthcare industry participation without a full rebuild.
• Path Dependency: Establishes the operational patterns for fully decentralized health AIs.
• Temporary Primitive: Will be subsumed by more efficient ZK and FHE stacks in 5-7 years.