DESILO Harvest™
It is designed to break down data silos by enabling complex computations directly on encrypted data in a zero-trust environment.
It ensures rapid, secure analysis between organizations without ever exposing sensitive, sovereign data.
DESILO Harvest™ unleashes a high-velocity R&D environment designed for the future of biomedical research.
Value
We deliver a streamlined, high-speed multi-party data collaboration experience
Access Untapped Data
Overcome regulatory and security barriers that have siloed sensitive genomic and clinical data.
Our homomorphic encryption and federated learning enable secure, collaborative research without exposing or moving raw data.
Generate Actionable Insights
Leverage larger, more diverse datasets to improve the accuracy and reliability of your AI models.
Our solution helps you uncover new drug targets and develop personalized therapies
—translating directly into tangible business outcomes.
Create Value from Your Own Data
Maintain full control of your data while performing advanced model training and analysis.
Our platform breaks down data silos and enables secure collaboration and AI innovation, even in a zero-trust environment.
Core Features
Homomorphic-Encryption–Based Secure Computation Engine
Performs computations—such as GWAS statistics (frequencies, chi-squared tests) and training of machine-learning models (linear/logistic regression)—without ever decrypting the data.
Developer SDKs & API
Python- and Java-based SDKs plus a RESTful API make it easy to integrate secure computation steps into existing analysis pipelines. High-level functions (e.g., "run GWAS on encrypted data") are abstracted for simplicity.
Audit & Compliance Dashboard
Records detailed logs of who accessed which encrypted data and what computations were performed, and automatically generates reports to demonstrate compliance with HIPAA, GDPR, and other regulations.
Privacy-Preserving Federated Learning
Train a central AI model across multiple institutions without centralizing data. The system aggregates only model updates, keeping individual datasets private.
Strength
Technology Leadership
The DESILO Secure Genomic Computation Engine is built on FHE.DESILO, our proprietary, production-grade Fully Homomorphic Encryption (FHE) library, architected to meet the computational demands of modern genomic analysis.
Symbiotic Market Positioning
Cloud providers (AWS, Azure, GCP) supply the "secure vault" via confidential computing. We provide the application layer that enables high-value scientific work inside that vault, unlocking the full potential of their secure infrastructure for life sciences.
Production-Grade Performance
Our full-stack commercial architecture was proven on real-world pharmaceutical workloads in the iDASH 2020 competition—trusted, third-party validation that de-risks adoption for partners.
Use Case
01. Pharmaceutical R&D
Securely unite multi-institution cohort data to run GWAS and regression analyses on encrypted data—achieving greater statistical power without sharing raw patient data.
02. CROs & Clinical Trials
When training data is limited by contractual firewalls, federated learning enables collaborative training of a global model by sharing only securely aggregated updates, not proprietary data—yielding more accurate, generalizable predictions.
03. Research Consortia
Support joint GWAS across member institutions while each retains full data sovereignty—no data movement or disclosure required.
01. Pharmaceutical R&D
Securely unite multi-institution cohort data to run GWAS and regression analyses on encrypted data—achieving greater statistical power without sharing raw patient data.
02. CROs & Clinical Trials
When training data is limited by contractual firewalls, federated learning enables collaborative training of a global model by sharing only securely aggregated updates, not proprietary data—yielding more accurate, generalizable predictions.
03. Research Consortia
Support joint GWAS across member institutions while each retains full data sovereignty—no data movement or disclosure required.
