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.

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.