# Overview

### DeSci for Biomedical Research

To address the challenges outlined earlier, we introduce an integrated framework for **Biomedical Decentralized Science (BioDeSci)** that combines:

* Secure data exchange
* Private model collaboration
* Verifiable computation

This end-to-end system is structured around three coordinated levels: **Data**, **Model**, and **Evaluation**, as illustrated below.

#### Core Components

* **Data Level**\
  A decentralized biomedical data marketplace allows hospitals, pharmaceutical firms, and academic labs to share encrypted datasets. Tokenized metadata and privacy-preserving smart contracts govern access. The system supports secure upload, fine-grained access control, and verifiable provenance—without ever disclosing raw patient information.
* **Model Level**\
  Rexis applies **Equivariant Encryption (EE)** to enable decentralized fine-tuning of large biomedical models (e.g., transformers). Each institution performs encrypted training locally. Gradients are securely aggregated into a global model, without exposing raw data, intermediate representations, or model internals.
* **Evaluation Level**\
  EE also enables encrypted inference. Outputs are verified via on-chain commitments, such as hash references and lightweight cryptographic proofs. External auditors can confirm model integrity and output correctness—without decrypting private data.

***

Together, these components form a **scalable, verifiable, and privacy-preserving framework** for biomedical AI. The system:

* Preserves confidentiality
* Guarantees computational integrity
* Facilitates reproducibility across institutions

These properties are essential for regulatory compliance and scientific credibility in real-world biomedical deployments.

<figure><img src="/files/rLav9geBujwQ9LJNab35" alt=""><figcaption><p>Overview of Data, Model, and Evaluation Levels. Our end-to-end pipeline operates across three coordinated layers: (1) Data Level: Hospitals, research labs, and pharmaceutical companies contribute encrypted biomedical datasets to a decentralized data marketplace, with blockchain-backed tokenization and access control; (2) Model Level: Equivariant Encryption (EE) enables privacy-preserving fine-tuning and secure aggregation of encrypted models across institutions without exposing raw data; (3) Evaluation Level: Encrypted inference is performed with verifiable integrity via lightweight cryptographic proofs and on-chain audit records, ensuring correctness and privacy.</p></figcaption></figure>


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