Your genome can be used to advance understanding of disease and human health...

...but it also reveals private information about you.

Are you a known criminal?

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 it's least secure node.

Compromised system illustration
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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.

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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.

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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 generallyrisk adverse. 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 identify a patient nor their associated institution.

Step Three

ZK 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, performs 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 smart phone 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 patients hardware ran the inquiry program correctly using data threat originated from one of 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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