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The field of genomics has come a long way in balancing the benefits of data sharing with privacy.
We've built federated networks– independently operated systems with a common language; to store patient data with their trusted health care provider, while letting researchers access it for approved uses.
But as the size of a federated system grows, it gets harder to maintain the level of security that sensitive health information warrants.
The system is only as secure as its least secure node.
Data exfiltration
A breach in a node in the federation allows a malicious party to exfiltrate data. Research has surfaced the potential for membership inclusion and genome reconstruction attacks as threats that need to be addressed.
Jurisdictional risk
Genomic research must transcend national borders to capture the full diversity of the human species. But a federated network with nodes operating across jurisdictions risks data collected by foreign intelligence organizations. In some locations, domestic law enforcement may also pressure institutions to reveal identifying information on citizens.
Honeypot
Even within a federated network, large quantities of data concentrate inside centralized institutions making them attractive targets. Over time the expense of storing data grows, and vigilant monitoring is needed to prevent unauthorized access.
So What?
Health and research institutions are generally risk-averse. In light of these threats, they avoid opening pathways to exchange patient data; even when doing so would lead to immense public good.
At the same time, a lack of data is often cited as a major bottleneck in the advancement of genomics research.
A public permissionless complement to federated genomic networks
New cryptographic primitives have enabled novel ways of creating decentralized verifiable systems with strong privacy and data integrity guarantees.
One such technology is zero-knowledge proving systems, which enable verification of computation performed on untrusted hardware.
Here's how this could be applied to the context of genomic research:
Step One
Give patients their data
Transfer custody of a patient's raw genomic data to them via a specialized third party service or a purpose built hardware device with secure enclave they connect to their home wifi.
Step Two
Public Attestations
Institutions publish attestations (signed messages using a private key) that represent the digital fingerprint of a patient's genome and phenotype. This proves the underlying data originated from a given institution.
Notably, attestations contain no information that identifies a patient nor their associated institution.
Step Three
Zero-knowledge cryptography
Researchers use an IDE to create inquiries that run within a zero-knowledge (ZK) virtual machine. In practice these inquiries are functions that accept an array of requested alleles and phenotypes as parameters, perform arbitrary computation and return a result.
Example Code
pub fn predict(
&self,
snp_data: &[SnpData],
hpo_codes: Option<&[&str]>
) -> Result<InquiryResult, InquiryError> {
let mut hpo_match = false;
let mut hpo_confidence = 0.0;
for model in &self.snp_models {
...
}
Ok(InquiryResult {
hpo_match,
hpo_confidence,
})
}Step Four
Publish Inquiry
The researcher publishes their compiled inquiry alongside metadata identifying them, their institution and a description of the inquiry in order to obtain informed consent from a patient.
The researcher may choose to limit inquiry responses to patients whose data has been attested to by one or more trusted sources.
Step Five
Patient Response
A patient's hardware device detects the newly published inquiry and notifies the patient's smartphone that they are eligible to respond.
The patient reviews the details of the inquiry, assisted by software that helps them understand the privacy implications and checks a global registry to verify the identity of the researcher.
If the patient consents, their hardware device executes the inquiry and transmits the results along with a zero-knowledge proof back to the researcher, using fully encrypted peer-to-peer technology.
Step Six
Verification
When a researcher receives a response, the provided zero-knowledge proof is verified. This verification guarantees that the patient's hardware ran the inquiry program correctly using data that originated from one of the trusted institutions.
Notably, verification does not reveal identifiers that link a patient across inquiries, nor does it reveal which institution the data originated from, nor when the data was created. It does however prevent patients from submitting duplicate responses.
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