New testable hypothesis: "Zero-knowledge proofs of geospatial provenance assertions (e.g., 'this image was taken within 10km of a clinical trial site') can be integrated with federated transcriptomic datasets to enable privacy-preserving validation of spatial correlations between environmental exposures and MS-associated gene expression (e.g., CTSS pathway activity) without revealing raw loca

A zero-knowledge proof (ZKP) protocol can attest that a biospecimen/metadata record originated within a bounded geographic region (e.g., "within 10km of clinical trial site X" or "within an EPA-defined pollution exposure zone") — verifiable by a third party with cryptographic soundness ≥99.99% (false-accept rate) and completeness ≥99% — while the verifier learns nothing about the exact coordinates beyond membership in the disclosed region (statistical distinguishability advantage <0.01 between true and simulated proof transcripts). Further, this ZKP-gated geolocation attribute can be joined with federated transcriptomic pipelines (e.g., CTSS/DNMT1/ZNF740 expression from GSE193770/GSE138614-style cohorts) such that a spatial correlation analysis (e.g., regional pollution index vs. CTSS log2FC) computed over ZKP-verified-but-location-hidden records produces effect estimates within 10% relative error of the same analysis computed with raw disclosed coordinates, on a synthetic/held-out ground-truth dataset.

Minimum Viable Test (MVT): A two-arm synthetic-data benchmark.

• Arm A (ground truth): Simulate N=5,000 synthetic patient records with (lat/lon, simulated CTSS expression value drawn from a model calibrated to GSE138614 effect sizes + spatial exposure gradient with known ground-truth correlation r=0.3).
• Arm B (ZK-gated): Same records, but location replaced by a ZKP attestation of "within region R_i" for 20 predefined regions; raw coordinates withheld from the analysis engine.
• Compute Pearson/Spearman correlation and mixed-effects regression (CTSS ~ exposure_proxy + region_random_effect) in both arms; compare coefficients, CIs, and p-values.
• Separately run cryptographic benchmarks: proof generation time, verification time, proof size, soundness/completeness under fuzzing (10,000 malicious proof attempts with off-region coordinates).

# --- Circuit definition (circom-like pseudocode) ---
template RegionMembership() {
    signal input lat;         // private
    signal input lon;         // private
    signal input region_id;   // public
    signal input region_center_lat; // public
    signal input region_center_lon; // public
    signal input radius_km;         // public
    signal output valid;

    dist <== haversine(lat, lon, region_center_lat, region_center_lon);
    valid <== LessThan(dist, radius_km);
}

# --- Client side (per patient) ---
def generate_proof(true_lat, true_lon, region):
    witness = {lat: true_lat, lon: true_lon, region_id: region.id, ...}
    proof = snark_prove(circuit=RegionMembership, witness=witness)
    return proof, region.id  # coordinates never leave client

# --- Federated aggregation server ---
def ingest_record(proof, region_id, ctss_value, covariates):
    assert snark_verify(proof, public_inputs=region_id)
    db.insert(region_id, ctss_value, covariates)  # no raw coords stored

def run_analysis():
    df = db.fetch_all()  # only region_id + expression + covariates
    model = mixedlm("ctss_value ~ exposure_index(region_id) + covariates",
                     groups=df.region_id)
    return model.fit()

# --- Adversarial fuzz harness ---
for trial in range(10000):
    fake_coords = perturb_outside_region(true_coords, region)
    proof = attempt_forge_proof(fake_coords, region)
    assert not snark_verify(proof)  # must fail

# --- Privacy attack harness ---
train_membership_inference_classifier(proof_transcripts, auxiliary_metadata)
measure_AUC_vs_baseline()