Consent Orchestration

Enables patients to specify permissions at a data element level rather than broad, all-or-nothing consent forms. The system captures explicit opt-in or opt-out choices for specific data types—such as genomic sequences, MRI scans, or prescription histories—and maps them to standardized FHIR consent resource profiles.

• Supports dynamic consent models where patients can modify permissions in real time
• Captures the legal basis for processing (e.g., explicit consent, legitimate interest, public interest)
• Integrates with Self-Sovereign Identity frameworks for patient-controlled credential presentation

Distributes consent directives across all participating institutions in a federated network within milliseconds of a permission change. When a patient revokes consent for a specific use, the orchestration layer broadcasts a cryptographically signed revocation notice to every node holding that patient's data or model updates.

• Uses a pub/sub messaging pattern over mutually authenticated TLS channels
• Maintains a conflict-free replicated data type (CRDT) for eventual consistency of policy state
• Integrates with blockchain audit trails to immutably record every policy propagation event

Embeds enforcement logic directly into the federated training pipeline through Policy Enforcement Points (PEPs) that intercept data access requests. Before any local model training or data extraction begins, the PEP queries the central Policy Decision Point (PDP) to validate that the requested operation matches the patient's active consent directives.

• Implements the XACML or Open Policy Agent (OPA) architecture for attribute-based access control
• Returns explicit Permit, Deny, or Not Applicable decisions with obligation metadata
• Prevents data exfiltration by blocking queries that exceed the patient's privacy budget

Cryptographically binds each data usage event to a specific, pre-authorized purpose of processing. The orchestration layer issues short-lived, purpose-scoped tokens that nodes must present when contributing to a federated training round. If a node attempts to use data for secondary research not covered by the original consent, the token validation fails.

• Leverages JSON Web Tokens (JWT) with embedded purpose claims and expiration timestamps
• Enforces data minimization by limiting token scope to only the necessary data attributes
• Aligns with GDPR Article 5(1)(b) purpose limitation principle

Generates a complete, tamper-evident record of every consent transaction—from initial capture through modification to revocation. Each state change is hashed and linked to the previous entry, forming a Merkle tree that makes retrospective alteration computationally infeasible.

• Provides compliance officers with a chain of custody report for any patient's consent journey
• Supports zero-knowledge proofs to demonstrate compliance without exposing raw consent data
• Enables automated Data Protection Impact Assessment (DPIA) validation against actual processing activities

Orchestrates the technical execution of GDPR Article 17 deletion requests across a decentralized network. When a patient invokes the right to erasure, the system identifies all model checkpoints and aggregated updates influenced by that patient's data and triggers machine unlearning or influence function removal procedures at each affected node.

• Maintains a data provenance graph mapping patient records to specific model weight contributions
• Coordinates the deletion of pseudonymized records and their linkages across institutional boundaries
• Issues cryptographic attestations confirming successful erasure for regulatory reporting

A mathematical framework that injects calibrated noise into model updates to provide provable privacy guarantees. When integrated with consent orchestration, differential privacy ensures that even when a patient grants permission for their data to participate in training, the system bounds the information leakage through the epsilon privacy budget. This creates a quantifiable link between a consent record and the statistical risk of re-identification.

A decentralized identity model where patients hold their own verifiable credentials without relying on a central authority. Consent orchestration leverages SSI to enable cryptographically signed consent tokens that travel with data across federated nodes. Key capabilities include:

• Decentralized identifiers (DIDs) for each patient
• Verifiable presentations proving consent scope
• Revocation registries for instant permission withdrawal

An immutable, append-only ledger that records every consent transaction, data access event, and model update in a federated network. Each consent grant or revocation is timestamped and cryptographically linked to previous entries via Merkle tree structures, creating a tamper-evident chain of custody. This provides regulators with a verifiable provenance trail demonstrating that data usage strictly matched the permissions granted at the time of collection.

A GDPR-mandated requirement (Article 17) enabling individuals to demand complete deletion of their personal data. In federated learning, this creates a profound technical challenge: unlearning the influence of a patient's data from trained neural network weights. Consent orchestration systems must track which model checkpoints incorporated which consent grants, enabling selective retraining or machine unlearning algorithms to honor withdrawal requests without degrading global model performance.

The legal principle that digital patient information is governed by the jurisdictional laws of the country where it is collected. Consent orchestration must dynamically enforce geographic constraints on data processing:

• EU patient data remains within GDPR-bound nodes
• Cross-border transfers require Standard Contractual Clauses
• Data residency flags prevent unauthorized geographic movement This ensures a consent grant in one jurisdiction does not violate the sovereignty requirements of another.

A cryptographic protocol enabling multiple distrusting parties to jointly compute functions without revealing their private inputs. In consent orchestration, SMPC allows privacy-preserving consent verification where a node can confirm a patient's permission status without accessing the full consent record. The computation proves compliance without exposing the underlying granular permissions, maintaining patient confidentiality even during the authorization check itself.