Empowering collaborative and secure healthcare AI innovation
Collaborative. Powerful. Federated.
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How it Works

How the Rhino Federated Computing Platform works

Transforming healthcare with collaborative, secure, and privacy-first AI

Data transformation & exploration hub
Data transformation & exploration hub

Secure federated analysis: Conduct exploratory data analysis on Federated Datasets to drive insights while maintaining privacy.

AI-enabled harmonization: Use AI to harmonize data into the precise standards needed with tools like Harmonization Copilot.

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Global federated network & collaboration

Diverse data discovery: Access and contribute to a global catalog of multimodal Federated Datasets.

Secure data collaboration: Provide secure and controlled access to partners so they can collaborate with your data and foster innovation.

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Agile deployment & access

Secure code deployment: Use your existing code and deploy it securely to be executed on datasets across the Rhino Federated Network.

Fine-grained access control: Define role-based access control (RBAC) for fine-grained permissions and access to collaborative projects.

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MLOps & Federated Learning framework

Federated MLOps ecosystem: Create AI models with Federated Learning frameworks for privacy-centric model training across datasets.

Workflow optimization: Manage complex Federated Training and Validation cycles efficiently and securely.

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Core Capabilities of the Rhino Federated Computing Platform

Collaborative, seamless, and privacy-centric tools to unlock the value of your data

Our Rhino Federated Computing Platform orchestrates complex projects tailored to the diverse needs of data scientists, researchers, and developers

Project management for collaboration and innovation
Granular access control

The Rhino Federated Computing Platform supports granular role-based access control (RBAC), enabling multi-site collaborative projects and internal team initiatives with complete control over access and permissions.

Full transparency and accountability

Maintain security safeguards and comprehensive audit logs to secure complex code and data environments efficiently, boosting project transparency and accountability.

Data management with uncompromised compliance
Robust data integration and management

Harness diverse data sources with simplified integration backed by robust versioning and auditability. Our data segregation techniques ensure high-quality, compliant datasets.

Global collaboration with local compliance

Our architecture, optimized for edge processing, fosters global collaboration without compromising data privacy. We adhere to rigorous standards such as ISO 27001, SOC 2 Type II, HIPAA, and GDPR, ensuring comprehensive security and compliance.

Secure, federated code execution
Secure code deployment

Code is deployed to the data’s processing locations in secure containers. It is executed in a privacy-enforcing sandbox to prevent data leakage.

Interactive and flexible computing

Access and manipulate data across the Rhino Federated Network through an intuitive web interface or programmatically via SDK or REST API. Our platform facilitates seamless data preprocessing, harmonization, and explanatory analysis through interactive containers.

Seamlessly use Federated Learning and MLOps
Scalable AI solutions with data sovereignty

Develop robust, scalable AI solutions using Federated Learning frameworks such as NVIDIA's FLARE™  and privacy-preserving techniques, including differential privacy and secure multi-party computation. Our data sovereignty-first approach ensures that data compliance and integrity are never compromised.

Streamline AI lifecycle management

Enhance the development and deployment of AI models with MLOps, integrating common third-party tools to manage models across distributed environments.

Explore our Federated Computing use cases for smarter solutions

Harmonization Copilot

Streamlining Health Data Harmonization Across Global Federated Networks with Harmonization Copilot

Use AI-enhanced tools to swiftly harmonize healthcare data into standard vocabularies and data models, enabling seamless interoperability and efficient data usage

Rhino’s Harmonization Copilot simplifies data harmonization through AI-driven tools that convert diverse healthcare data into widely adopted common data models, such as Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR). This application is pivotal for global research collaborations, ensuring data consistency and compatibility across healthcare systems. The Harmonization Copilot protects data privacy by processing information locally on the Rhino Client. This automation significantly reduces the time and effort required for data preparation, accelerating time-to-market for healthcare innovations, and enhancing the overall efficiency of medical research operations, thereby contributing to the global healthcare community.

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Enhance interoperability with Generative AI, rapidly adapting data into standards like OMOP and FHIR while ensuring data privacy.

Transform data faster, shorten the harmonization process from months to weeks, and increase research efficiency and innovation pace.

Process data at its origin, and use localized computation to maintain sovereignty and reduce privacy and compliance risks.

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Discover Real-World Insights
Through Federated Datasets

Access and publish real-world datasets for collaborative research and development across a global federated network

Rhino’s Federated Datasets empower healthcare institutions to engage in extensive multi-site research without data transfer, preserving patient confidentiality and compliance with regulations like GDPR and HIPAA. The Rhino Federated Computing Platform enables healthcare providers and researchers to contribute to and benefit from a rich pool of datasets, such that the underlying data is stored locally at each site and adheres to the site’s strict privacy and compliance controls.

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The collaborative approach allows controlled access to data aggregations while maintaining data privacy, thus improving the quality and generalizability of research findings and driving medical research and patient care.

Discover Federated Datasets

Tap into extensive and diverse real-world datasets for advanced research across our global federated network.

Enable seamless collaboration without compromising data privacy through secure, federated data access.

Process data at its origin, and use localized computation to maintain sovereignty and reduce privacy and compliance risks.

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Simplifying the Complex Processes withRemote Data Viewers and Annotation

Empower global collaboration on data annotation with secure and privacy-preserving annotation tools

Rhino’s Remote Data Viewers and Annotation allow researchers and specialists to remotely and collaboratively annotate data without transferring the data, thus preserving privacy and enhancing the data’s relevance and value. The viewers are executed remotely on data under strict privacy and permissions controls, without storing this data, or the resulting annotations, anywhere outside of its source of origin. This federated approach protects patient privacy and uses the collective expertise of global specialists, improving diagnostic accuracy and facilitating faster, more reliable medical responses. Integrating Remote Annotation allows enriching training and validation of datasets for AI models without compromising data security or compliance.

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Dynamic, secure viewing within local data confines, enabling protected data review and annotation.

Integrate your preferred annotation tools.

Share access with collaborators, maintaining data privacy with temporary, controlled data access.

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Developing Scalable AI Models with Federated Training and Validation

Advance model accuracy and generalizability across diverse healthcare applications

Use advanced out-of-the-box capabilities for Federated Training and Validation to develop and refine AI models with data spanning multiple sites. This approach safeguards patient data and utilizes diverse datasets to improve model accuracy and reliability. Instead of rolling out existing or custom Federated Learning SDKs, Rhino Federated Computing Platform provides a managed platform for all your Federated Learning needs, including adherence to strict InfoSec and networking requirements at different sites, maintaining security compliance, and continuously updating the platform capabilities.

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Robust end-to-end platform accelerates federated AI model training and validation across distributed data.

Manage multiple experiments efficiently, tuning and testing across diverse datasets.

Real-time monitoring with MLOps tools such as TensorBoard to improve model performance.

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Advancing Real-World Data Studies with Federated Statistical Methods and Analytics

Perform comprehensive biostatistical analysis across multiple datasets without compromising privacy

The Rhino Federated Computing Platform offers a robust package of Federated Statistical Methods and Analytics tools, allowing statisticians to conduct advanced statistical analysis on federated datasets. This capability is vital for analyzing real-world data and providing in-depth insights that enhance research on observational studies and health outcomes. By enabling the execution of complex biostatistical methods such as survival analysis and risk adjustment within a federated framework, Rhino Health ensures that data remains secure and private, enhancing the integrity and applicability of research outcomes while allowing research to be conducted across data silos.

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Apply statistical methods and calculate biostatistical metrics across datasets from multiple sites while preserving privacy.

Utilize customizable regressions, risk adjustment models, and survival analysis methods  for deeper data insights.

Expand analytics capabilities with custom metrics and visualizations tailored to research needs.

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Privacy and Security

Security and data privacy preserved every step of the way

Strict data processing safeguards

Local data stays local: Data stays within custodian firewalls and is only processed locally. Only aggregated data insights and global model weights are shared.

Sandboxed code execution: Data processing involves executing pre-approved code in a sandbox that blocks all network access, preventing data exfiltration. Data is encrypted at rest and in transit, incorporating homomorphic encryption to allow for computation on encrypted data.

Secure and adaptable deployments

Flexible deployment options: Rhino Clients can be deployed on-premise in your data center or within your cloud account such as Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure. This flexibility ensures you maintain complete control over your data and code

Preserving data sovereignty: You are not changing your data residency, while data and intellectual property remain exclusively yours.

Audit logging and access management

Comprehensive audit trails: All user actions and access are meticulously recorded, offering usage visibility and compliance with security best practices.

Customizable role-based permissions: Granular role-based access control (RBAC) and permissions can be configured per project, ensuring only authorized users can access any type of data or process.

Robust privacy and compliance measures

Differential privacy and de-identification protocols: Include differential privacy strategies, tailored de-identification techniques, and k-anonymization to enhance data privacy.

Global standards compliance: Following the highest international standards, Rhino Health is certified with ISO 27001 and SOC 2 Type II, with practices reviewed for HIPAA and GDPR compliance.

Securing trust with global standards

Rhino Health Trust Center

Empowering secure health data collaborations

Secure, compliant, and transparent operations build the foundation of our Trust Center.

Certified and compliant

Our platform meets ISO 27001, SOC 2 Type II, HIPAA, and GDPR standards.

Data privacy first

Federated Computing and Federated Learning ensure patient data stays private and secure.

Trust Center
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