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.

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.

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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

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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.

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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.

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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.