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Privacy

DT4H privacy posture centers on data minimization, clear boundaries, and responsible longitudinal modeling.

Privacy principles for Digital Twin for Health infrastructure inside the AvatarK.ai ecosystem.

Privacy diagram #

COLLECTMinimum Signals

Only data required for modeling, calibration, and execution context

→
MODELBounded Twin

Cohort context · Twin state · confidence · calibration history

→
CONTROLAccess Boundary

Consumer · clinician · researcher · operator visibility separation

→
GOVERNConsent Context

Research · care · consumer execution · platform operations

Privacy responsibilities #

01

Data minimization

Collect only what is necessary for modeling, calibration, and runtime operation.

02

Purpose boundaries

Separate infrastructure modeling from research, care, and consumer execution contexts.

03

Access controls

Sensitive workflows should use role-based access, least privilege, and auditability.

04

Longitudinal integrity

Health-related runtime signals require careful handling across time.

05

Consent context

Use of personal or health-related data should follow applicable consent and governance requirements.

06

Clinical separation

Modeling outputs are not autonomous diagnosis or treatment decisions.

Privacy-sensitive runtime surfaces #

SignalsBiomarker, behavior, clinical, and self-report data may be sensitive.
CohortsPopulation-context assignment should not become identity labeling.
Twin stateLongitudinal model state requires careful access boundaries.
Calibration historyEvidence updates can reveal behavior and outcome patterns over time.
SETPOINT feedbackExecution outcomes should be handled according to context and consent.

Boundary principles #

Minimum necessaryReduce exposure

Modeling systems should avoid unnecessary signal collection.

ContextSeparate uses

Research, care, and consumer execution should remain distinguishable.

AccessRole bounded

Runtime and Twin data access should be scoped by responsibility.

GovernanceExplicit review

Longitudinal health-related systems require ongoing governance.

Implementation notes #

Minimize longitudinal exposure

Only collect and retain signals needed for modeling, calibration, and execution context.

Separate context from identity

Cohort assignment should support modeling without becoming a permanent identity label.

Design consent by workflow

Research, consumer, care, and operator workflows should have distinct consent and visibility boundaries.

LayerGovernance Framework
StatusActive Draft
SystemDT4H / StateK / SETPOINT
BoundaryInfrastructure, not diagnosis
System lineageDT4H→Twin→StateK→SETPOINT→Outcomes→Recalibration
Infrastructure boundaryDT4H models cohorts, Twins, calibration, and runtime state. It does not diagnose, prescribe, or replace licensed clinical judgment.
Document statusInfrastructure draft
Last updatedMay 2026
Applies toDT4H.ai / AvatarK.ai ecosystem