Post-quantum cryptographic techniques for message transformation can be applied to secure the transmission of sensitive transcriptomic data in Multiple Sclerosis research across distributed networks.

Post-quantum cryptographic (PQC) algorithms — specifically lattice-based (e.g., CRYSTALS-Kyber/CRYSTALS-Dilithium, NIST PQC Round 3 finalists) and hash-based (e.g., SPHINCS+) schemes — can encrypt, sign, and transmit RNA-seq transcriptomic datasets (≥10,000 gene features, ≥50 patient samples) characteristic of Multiple Sclerosis (MS) research across geographically distributed nodes with: (a) no statistically significant loss of data integrity (bit-error rate = 0), (b) end-to-end latency overhead ≤15% compared to classical AES-256/RSA-2048 baselines, (c) computational overhead ≤3× classical methods on commodity hardware, and (d) resistance to both classical and quantum adversarial attacks as defined by NIST security levels I–V.

PHASE 1 — Baseline Characterization (Days 1–15): Establish performance benchmarks for classical cryptography (AES-256-GCM + RSA-2048) on MS transcriptomic datasets. Measure throughput, latency, CPU utilization, and memory consumption.

PHASE 2 — PQC Implementation and Unit Testing (Days 16–35): Implement PQC pipeline using liboqs (Open Quantum Safe library) with Kyber-768 for key encapsulation and Dilithium3 for digital signatures. Unit test on synthetic RNA-seq data (simulated via polyester R package).

PHASE 3 — Integration Testing on Real MS Data (Days 36–60): Apply PQC pipeline to publicly available MS transcriptomic datasets (GEO accession GSE138614, n=107 samples; GSE41850, n=140 samples). Measure all performance metrics.

PHASE 4 — Distributed Network Simulation (Days 61–90): Deploy multi-node testbed using Docker/Kubernetes across 3 geographic cloud regions (US-East, EU-West, Asia-Pacific). Simulate adversarial conditions (packet loss 1–5%, latency injection 50–200 ms).

PHASE 5 — Security Audit and Penetration Testing (Days 91–110): Conduct formal security analysis including fuzzing, side-channel timing analysis, and simulated quantum adversary (Grover's algorithm simulation on reduced key sizes).

PHASE 6 — Biological Validity Verification (Days 111–120): Confirm that DGE analysis, pathway enrichment (GSEA), and co-expression network (WGCNA) results are statistically identical pre- and post-encryption/decryption.

# PQC Transcriptomic Data Transfer Pipeline
# Architecture: Hybrid KEM + Symmetric Encryption + Digital Signature

## SYSTEM ARCHITECTURE
"""
[Data Source Node]          [Transit Layer]         [Recipient Node]
  RNA-seq Data               PQC-TLS 1.3              Decryption
  (FASTQ/BAM/HDF5)    -->   Kyber-768 KEM    -->     Verification
  Chunking (1GB)             AES-256-GCM              DGE Analysis
  Dilithium3 Sign            gRPC/HTTPS               Audit Log
"""

## PSEUDOCODE

# Step 1: Key Setup (run once per session)
def setup_pqc_session(sender_id, receiver_id):
    # Generate Kyber-768 keypair for receiver
    receiver_pk, receiver_sk = kyber768.keygen()
    
    # Generate Dilithium3 keypair for sender (signing)
    sender_sign_pk, sender_sign_sk = dilithium3.keygen()
    
    # Exchange public keys via authenticated channel
    # (bootstrapped with classical PKI, migrated to PQC PKI)
    register_public_key(receiver_id, receiver_pk)
    register_public_key(sender_id, sender_sign_pk)
    
    return sender_sign_sk, receiver_pk

# Step 2: Data Preparation
def prepare_transcriptomic_data(filepath, chunk_size_gb=1):
    data = load_genomic_file(filepath)  # FASTQ/BAM/HDF5
    chunks = split_into_chunks(data, chunk_size_gb * 1024**3)
    checksums = [sha256(chunk) for chunk in chunks]
    manifest = create_manifest(filepath, checksums, timestamp=now())
    return chunks, manifest

# Step 3: Encryption (per chunk)
def encrypt_chunk_pqc(chunk, receiver_pk, sender_sign_sk):
    # KEM: encapsulate shared secret
    ciphertext_kem, shared_secret = kyber768.encapsulate(receiver_pk)
    
    # Derive AES key from shared secret
    aes_key = hkdf_sha256(shared_secret, salt=os.urandom(32), 
                           info=b"MS-transcriptomics-v1", length=32)
    
    # Encrypt chunk with AES-256-GCM
    nonce = os.urandom(12)
    ciphertext_data, tag = aes_256_gcm_encrypt(aes_key, nonce, chunk)
    
    # Sign the encrypted chunk
    payload = ciphertext_kem + nonce + ciphertext_data + tag
    signature = dilithium3.sign(payload, sender_sign_sk)
    
    # Package
    encrypted_chunk = {
        'kem_ciphertext': ciphertext_kem,    # 1088 bytes (Kyber-768)
        'nonce': nonce,                       # 12 bytes
        'data_ciphertext': ciphertext_data,   # variable
        'aes_tag': tag,                       # 16 bytes
        'signature': signature,               # 3293 bytes (Dilithium3)
        'chunk_id': uuid4(),
        'algorithm': 'KYBER768-AES256GCM-DILITHIUM3'
    }
    return encrypted_chunk

# Step 4: Transmission
def transmit_encrypted_dataset(encrypted_chunks, manifest, 
                                 receiver_endpoint):
    session = establish_pqc_tls_session(receiver_endpoint)
    
    # Send manifest first
    send_with_retry(session, serialize(manifest), max_retries=3)
    
    # Stream chunks with flow control
    for i, chunk in enumerate(encrypted_chunks):
        ack = send_with_retry(session, serialize(chunk), max_retries=5)
        if not ack.success:
            raise TransmissionError(f"Chunk {i} failed after 5 retries")
        log_transfer_metric(chunk_id=chunk['chunk_id'], 
                           bytes_sent=len(chunk['data_ciphertext']),
                           latency_ms=ack.latency)
    
    return TransferReceipt(manifest_hash=sha256(manifest), 
                           total_chunks=len(encrypted_chunks))

# Step 5: Decryption and Verification
def decrypt_and_verify(encrypted_chunks, receiver_sk, 
                        sender_sign_pk, expected_manifest):
    decrypted_chunks = []
    
    for chunk in encrypted_chunks:
        # Verify signature first
        payload = (chunk['kem_ciphertext'] + chunk['nonce'] + 
                   chunk['data_ciphertext'] + chunk['aes_tag'])
        if not dilithium3.verify(payload, chunk['signature'], sender_sign_pk):
            raise SecurityError(f"Signature verification FAILED chunk {chunk['chunk_id']}")
        
        # Decapsulate shared secret
        shared_secret = kyber768.decapsulate(chunk['kem_ciphertext'], receiver_sk)
        
        # Derive AES key
        aes_key = hkdf_sha256(shared_secret, salt=..., 
                               info=b"MS-transcriptomics-v1", length=32)
        
        # Decrypt
        plaintext = aes_256_gcm_decrypt(aes_key, chunk['nonce'], 
                                         chunk['data_ciphertext'], chunk['aes_tag'])
        decrypted_chunks.append(plaintext)
    
    # Reassemble and verify integrity
    full_data = reassemble_chunks(decrypted_chunks)
    verify_manifest_checksums(full_data, expected_manifest)
    
    return full_data

# Step 6: Biological Validation
def validate_biological_integrity(original_path, decrypted_data):
    original = load_count_matrix(original_path)
    decrypted = load_count_matrix(decrypted_data)
    
    # Bit-level check
    assert sha256(original) == sha256(decrypted), "INTEGRITY FAILURE"
    
    # Biological-level check (DESeq2 via rpy2)
    dge_original = run_deseq2(original, design="~condition")
    dge_decrypted = run_deseq2(decrypted, design="~condition")
    
    jaccard = compute_jaccard(dge_original.sig_genes, dge_decrypted.sig_genes)
    pearson_r = correlate(dge_original.log2fc, dge_decrypted.log2fc)
    
    assert jaccard >= 0.99, f"Biological validity FAILED: Jaccard={jaccard}"
    assert pearson_r >= 0.9999, f"Fold-change correlation FAILED: r={pearson_r}"
    
    return ValidationReport(jaccard=jaccard, pearson_r=pearson_r, 
                            integrity="PASS")

## BENCHMARKING HARNESS
def run_benchmark_suite(dataset_sizes_gb=[1, 10, 50, 100], 
                         n_trials=10, crypto_modes=['classical', 'pqc']):
    results = []
    for size in dataset_sizes_gb:
        data = generate_synthetic_rnaseq(size_gb=size)
        for mode in crypto_modes:
            for trial in range(n_trials):
                t_start = time.perf_counter()
                if mode == 'pqc':
                    encrypted = encrypt_chunk_pqc(data, receiver_pk, sign_sk)
                    transmitted = transmit_encrypted_dataset([encrypted], ...)
                    decrypted = decrypt_and_verify([encrypted], ...)
                else:
                    encrypted = aes256_encrypt(data)
                    transmitted = transmit_classical(encrypted)
                    decrypted = aes256_decrypt(encrypted)
                t_end = time.perf_counter()
                
                results.append({
                    'size_gb': size, 'mode': mode, 'trial': trial,
                    'latency_s': t_end - t_start,
                    'throughput_mbps': (size*1024) / (t_end - t_start),
                    'cpu_pct': psutil.cpu_percent(),
                    'mem_gb': psutil.virtual_memory().used / 1e9
                })
    
    return pd.DataFrame(results)

## DEPLOYMENT CONFIGURATION (docker-compose excerpt)
"""
services:
  pqc-sender:
    image: pqc-transcriptomics:v1.0
    environment:
      - KYBER_SECURITY_LEVEL=768
      - DILITHIUM_LEVEL=3
      - CHUNK_SIZE_GB=1
      - MAX_RETRIES=5
    volumes:
      - /data/rnaseq:/data:ro
      
  pqc-receiver:
    image: pqc-transcriptomics:v1.0
    ports:
      - "8443:8443"  # PQC-TLS
    environment:
      - VERIFY_SIGNATURES=true
      - AUDIT_LOG=true
"""