for Biomedical Researchers

Privacy-First Collaboration for Biomedical Data

Biovault is an open-source platform that enables biomedical research across institutions without transferring sensitive data. It implements a data-visitation model in which approved analyses travel to data rather than data being moved to analysts.

The Challenge

Progress in precision medicine depends on collaboration between hospitals, biobanks and research groups. At the same time, privacy regulations and governance requirements increasingly restrict how biomedical data can be shared.

This creates a structural conflict:

Meaningful research requires combining datasets
Ethical and legal obligations limit data movement

Traditional solutions of centralized repositories, trusted research environments, and formal data-sharing agreements all often require significant resources and can reduce local control over sensitive information.

How BioVault Helps

BioVault offers a practical alternative: local execution instead of data transfer.

Researchers develop analyses using privacy-safe mock datasets that mirror the structure of private data. When ready, those analyses are submitted for approval. Approved workflows run inside the data owner’s environment, and only permitted results are returned.

This model enables:

Collaborative analysis without copying data

Preservation of institutional governance

Participation across jurisdictions

Use of existing computing environments

Raw biomedical data remain under the control of their owners at all times.

Supported Workflows

BioVault is designed to be domain-agnostic. It can support a wide range of biomedical applications, including:

Genomic and GWAS analyses

Single-cell and multi-omics workflows

Medical imaging studies

Clinical time-series analysis

Machine learning model evaluation

Analyses can be submitted as Jupyter notebooks or Nextflow pipelines and executed on laptops, servers, HPC systems, or cloud infrastructure.

Join Beta

Governance and Security Model

BioVault is built around practical institutional requirements:

Human-in-the-loop approval for each execution

Transparent audit trails

Fine-grained control over outputs

Computation performed locally within existing systems

Encrypted communication between collaborators

Data protection is achieved through local execution, controlled permissions, and auditable workflows, ensuring strong privacy without requiring data centralization.

Evidence From Practice

BioVault has already supported cross-institution collaborations on sensitive biomedical datasets. These projects showed that complex analyses such as genomic studies and machine learning workflows can be completed effectively without exporting patient data.

Open Infrastructure

BioVault is fully open source and built on the SyftBox protocol for decentralized, privacy-preserving computation. It integrates with existing tools and workflows, allowing institutions to participate without adopting new centralized platforms.

Join the Beta

Biovault is available for researchers and institutions seeking to collaborate responsibly on sensitive data.

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