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.
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.
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.
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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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.
Maintain security safeguards and comprehensive audit logs to secure complex code and data environments efficiently, boosting project transparency and accountability.
Harness diverse data sources with simplified integration backed by robust versioning and auditability. Our data segregation techniques ensure high-quality, compliant datasets.
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.
Code is deployed to the data’s processing locations in secure containers. It is executed in a privacy-enforcing sandbox to prevent data leakage.
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.
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.
Enhance the development and deployment of AI models with MLOps, integrating common third-party tools to manage models across distributed environments.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
