Proposed Group: Universal Health Data Schemas for Privacy-Preserving AI Community Group
The Universal Health Data Schemas for Privacy-Preserving AI Community Group has been proposed by Amir Hameed Mir:
The mission of this group is to define a universal, modular, and interoperable set of data schemas for health information. Our goal is to enable the aggregation and utilization of data for medical research and AI training through privacy-enhancing technologies (PETs) like Zero-Knowledge Proofs (ZKPs), while ensuring patient control and consent via Verifiable Credentials (VCs).
Scope and Problem Statement
The development of robust medical AI is hampered by siloed, non-standardized, and sensitive health data. Current data formats are incompatible across institutions, and privacy regulations prevent the sharing of raw data, creating a significant barrier to collaborative research. This group will address this by creating schemas that transform health data into standardized, verifiable, and privacy-preserving assets.
Key Deliverables
- A core set of modular, extensible Verifiable Credential schemas for common medical data types (e.g., lab results, imaging reports, prescriptions, diagnoses)
- Best practice guidelines for issuing these VCs from trusted sources (e.g., hospitals, clinics)
- Specifications for generating Zero-Knowledge Proofs from these VCs to enable privacy-preserving queries and analytics
- Use cases and implementation patterns for federated learning and AI model training using the proposed schemas and ZKP protocols
You are invited to support the creation of this group. Once the group has a total of 5 supporters, it will be launched and people can join to begin work. In order to support the group, you will need a W3C account.
Once launched, the group will no longer be listed as “proposed”; it will be in the list of current groups.
If you believe that there is an issue with this group that requires the attention of the W3C staff, please send us email on site-comments@w3.org
Thank you,
W3C Community Development Team