# Evaluation Level: Secure and Verifiable Biomedical Computation

### Evaluation Level: Secure and Verifiable Biomedical Computation

Although decentralized biomedical model fine-tuning can be achieved securely with **Equivariant Encryption (EE)**, verifiable and privacy-preserving evaluation remains an open challenge.

Institutions must not only compute correct results, but also **prove correctness** to regulators and collaborators—without revealing sensitive patient data or proprietary model details.

<figure><img src="/files/GudDw2np0VeqfxTZfXwH" alt=""><figcaption><p>Evaluation Level: EE-Enabled Encrypted Inference and Verification. The evaluator encrypts inputs and queries the deployed EE-protected model. Inference is performed entirely in the encrypted domain, and a lightweight proof π is optionally generated to attest correctness. Validators verify the model’s integrity via hash(A′) and log the proof and hash(y′) on-chain. Certified results are returned without exposing raw data or intermediate states.</p></figcaption></figure>

***

#### EE-Enabled Encrypted Inference and Verification

Given an encrypted input ( x' = T x + \delta ) and EE-transformed model parameters ( (A', b') ), inference proceeds as:

$$
y' = R(A' x' + b')
$$

No plaintext parameters or activations are revealed at any point.

The result is decrypted only by the requester:

$$
y = T^{-1}(y' - \delta)
$$

To guarantee correctness and model integrity:

* The **hash of the deployed model** ( \mathrm{hash}(A') ) is stored on-chain
* A **lightweight proof** ( \pi ) may be generated to link ( y' ) to the authorized model

***

#### Decentralized Certification Workflow

The evaluation pipeline proceeds through four stages:

1. **Encrypted Inference Query**\
   A user encrypts their private input ( x ) and submits ( x' = T x + \delta ) to the model host.
2. **On-Chain Model Verification**\
   Before inference, smart contracts or validators check that the model hash ( \mathrm{hash}(A') ) matches the authorized version.
3. **Inference and Optional Proof Generation**\
   The encrypted inference is computed as ( y' = R(A' x' + b') ), and the host may also produce a lightweight proof ( \pi ).
4. **Decryption and On-Chain Logging**\
   The requester decrypts the output, and optionally logs ( \mathrm{hash}(y') ) and ( \pi ) to the blockchain for audit purposes.

***

#### Security Considerations

* **Privacy Preservation**\
  Intermediate states and final outputs remain encrypted unless explicitly decrypted by the user.
* **Verifiability and Integrity**\
  Cryptographic links between ( A' ), ( y' ), and ( \pi ) allow third-party verification—without exposing internal model logic or data.
* **Efficiency and Practicality**\
  This approach avoids the computational burden of full ZK proofs. Only commitments (e.g., hashes) are logged, enabling real-time, scalable inference.

***

#### Benefits for Biomedical Applications

This framework supports cryptographically verifiable evaluation in sensitive biomedical domains, including:

* Genome-wide association studies
* Regulatory audit of pharmaceutical trials
* Clinical model validation across institutions

When combined with Rexis’s encrypted training and compute-to-data sharing, this final layer completes an **end-to-end, privacy-first platform** for decentralized biomedical AI.

It empowers institutions to share, train, and evaluate models—**without ever compromising data integrity or privacy**.


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