Securing the link
Every implant that talks wirelessly exposes a telemetry link. Unencrypted, it can be eavesdropped or spoofed; the defense is standard — authenticated encryption plus a fresh session key — packaged as secure neural telemetry. What is not standard is the budget it runs on.
Computing on ciphertext
What if the cloud could run your decoder without ever seeing your data? Homomorphic encryption (HE) allows exactly that — arithmetic performed directly on ciphertext, with the result decrypting to the correct plaintext answer.
The homomorphic property: encrypted operands can be added and multiplied, and the decryption of the encrypted result equals the plaintext operation.
The honest limit is cost. Homomorphic inference is orders of magnitude slower and heavier than plaintext, turning a millisecond decode into seconds or worse. It is a promising research direction for offline or non-urgent neural analytics, but incompatible with real-time closed-loop control today.
Enclaves at the edge
A cheaper middle path is a trusted execution environment (TEE): a hardware-isolated enclave that runs the decoder so that even a compromised operating system cannot read its memory, and can attest — prove cryptographically — which code is running. It pairs naturally with consent-preserving computation, where the enclave enforces the exact uses the user agreed to.
Defense in depth
Stack the layers and the earlier pipeline gains a real defense stage: minimize on-device, add differential-privacy noise, run the decoder in an enclave, and encrypt the link. No layer is complete, but together they force an attacker to defeat several independent controls.