To support privacy-preserving federated learning in genomics research, the TES API can be extended in several ways:
Data Encryption: Implement end-to-end encryption mechanisms to ensure that sensitive genomic data remains encrypted throughout the federated learning process. This would involve encrypting data at rest and in transit to protect patient privacy.
Secure Data Sharing: Develop secure data sharing protocols that allow different institutions to collaborate on genomic analysis without compromising data privacy. This could involve implementing secure data sharing agreements and access control mechanisms.
Differential Privacy: Integrate differential privacy techniques into the TES API to add noise to the data before sharing it for analysis. This ensures that individual data points cannot be re-identified while still allowing for meaningful analysis at a population level.
Secure Compute Environments: Ensure that the compute environments used for federated learning tasks are secure and compliant with data privacy regulations. This may involve using trusted execution environments or secure enclaves to process sensitive data.
Access Control: Implement robust access control mechanisms within the TES API to restrict access to sensitive data and ensure that only authorized users can perform certain operations on the data.
By incorporating these features into the TES API, researchers in genomics can leverage federated learning techniques while maintaining the privacy and security of sensitive genomic data.