Rexis: The L1 for DeSci
The first end-to-end platform for Biomedical Decentralized Science (BioDeSci).
Introduction to Rexis
Rexis is designed for a new era of biomedical discovery—one where large-scale machine learning on sensitive patient data is not just possible, but secure, verifiable, and decentralized.
It introduces the first end-to-end (e2e) platform that unifies:
Secure data sharing
Privacy-preserving model fine-tuning and training
Decentralized evaluation
within a single, privacy-first ecosystem (van Rossum et al., 2023).
Why Centralized Biomedical AI Falls Short
Today’s biomedical workflows remain fragmented, insecure, and siloed. Researchers often rely on ad hoc data transfers—via email, FTP, or cloud storage—which are incompatible with modern privacy standards like HIPAA and GDPR (Mora et al., 2022; Kim et al., 2021).
Clinical and genomic datasets are too sensitive to share, yet large-scale AI models require data diversity far beyond what any single institution can provide. Partial solutions—like federated learning (FL), differential privacy (DP), and secure enclaves (TEE)—offer incremental gains but suffer from high overhead, limited scalability, or weak guarantees (Abadi et al., 2016; Zhu et al., 2019; Costan & Devadas, 2016).
Without a purpose-built system for secure, collaborative computation across institutional boundaries, biomedical AI cannot reach its full potential.
The Rexis Architecture
At the core of Rexis lies Equivariant Encryption (EE)—a cryptographic method that transforms both model parameters and data into an encrypted space. EE supports operations like matrix multiplication and non-linear activation in the encrypted domain, enabling full ML pipelines to run without decrypting inputs, activations, or gradients (Gilad-Bachrach et al., 2016; Brutzkus et al., 2019).
🔐 Secure Data Sharing
Rexis enables a decentralized biomedical data marketplace. Institutions tokenize datasets as NFT-like digital assets with embedded on-chain metadata for provenance, ownership, and access policy enforcement.
Instead of exchanging raw files, researchers submit compute-to-data queries, which execute securely over encrypted data within trusted smart contract frameworks (Hasselgren et al., 2020). This ensures regulatory compliance and robust auditability—without ever revealing patient-level information.
🧠 Privacy-Preserving Model Fine-Tuning
Rexis supports secure fine-tuning of biomedical foundation models (e.g., BioBERT, ClinicalBERT) using encrypted electronic health records or genomic data (Lee et al., 2020; Alsentzer et al., 2019).
Each institution performs encrypted training locally and shares only encrypted gradient updates with a decentralized aggregator. No patient records, model weights, or activations are ever exposed—satisfying both fidelity and privacy requirements (Gu et al., 2021; Cho et al., 2022).
✅ Decentralized Evaluation
To support reproducibility and regulatory audit, Rexis implements verifiable inference. Models are evaluated on encrypted test data across multiple sources. The results are certified using lightweight cryptographic commitments and stored on-chain (Zhang et al., 2023; Mora et al., 2022).
This ensures transparency and trust—without compromising confidentiality.
Why Rexis Is Different
Rexis is the first platform to integrate sharing, training, and evaluation within a unified, cryptographically secure stack:
Data sharing
Tokenized assets + compute-to-data
IPFS or metadata-only solutions
Model fine-tuning
Fully encrypted via EE
Not supported
Verifiable evaluation
Encrypted inference + proofs
Not supported
Regulatory compliance
HIPAA/GDPR aligned design
Varies; typically incomplete
While other DeSci projects focus on governance or data funding (e.g., Molecule, VitaDAO), Rexis directly supports computation on sensitive biomedical data—making it usable in real-world research and clinical pipelines.
Representative DeSci Projects
Research storage
Cloud + AI
N/A
Secure data repository
Genomics collaboration
Blockchain peer review
N/A
Tokenizing IP for review
Longevity research
DAO funding
N/A
Crowdsourcing trials
Biotech IP
Tokenized IP + funding
N/A
Funding research projects
Patient data ownership
IPFS
Patient-controlled
Sharing data for rare disease studies
Rexis builds on these foundations by enabling not just collaboration and funding—but actual AI computation over encrypted biomedical data.
The Future of BioDeSci
Decentralized science (DeSci) offers a compelling vision: open, transparent, and equitable research infrastructure. But biomedical domains demand more—they require privacy, reproducibility, and regulatory integrity (van Rossum et al., 2023; Mora et al., 2022).
Equivariant Encryption (EE) makes this possible. By enabling compliant, cross-institutional computation without revealing raw data, Rexis supports:
Multi-site clinical research collaboration
Genomic and EHR-driven personalized medicine
Federated pipelines for drug discovery
On-chain model certification and reproducibility auditing
What Comes Next
This documentation covers:
The systemic barriers to trustworthy biomedical AI
The cryptographic design of Equivariant Encryption
Real-world case studies showing how Rexis enables private training and decentralized evaluation
Rexis sets a new standard for biomedical AI—making privacy the default, not the exception.
References
Abadi, M., et al. (2016). Deep learning with differential privacy. CCS.
Alsentzer, E., et al. (2019). ClinicalBERT embeddings. Clinical NLP Workshop.
Brutzkus, A., et al. (2019). Low latency privacy-preserving inference. ICML.
Cho, H., et al. (2022). Privacy-preserving genomic analysis. ARBMDS.
Costan, V., & Devadas, S. (2016). Intel SGX explained. IACR ePrint.
Gilad-Bachrach, R., et al. (2016). CryptoNets: Neural networks on encrypted data. ICML.
Gu, Y., et al. (2021). Biomedical domain-specific pretraining. ACM TCH.
Hasselgren, A., et al. (2020). Blockchain in healthcare. J Med Internet Res.
Kim, M., et al. (2021). Federated learning in medicine. JAMIA.
Lee, J., et al. (2020). BioBERT: Biomedical language models. Bioinformatics.
Mora, C., et al. (2022). Promises and pitfalls of DeSci. Science.
van Rossum, T., et al. (2023). Decentralized biomedical research. Nature Biotech.
Zhang, Y., et al. (2023). ZK proofs for privacy-preserving ML. ACM Computing Surveys.
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