Compliant analysis on patient data that stays confidential
Set research free from data silos while sensitive health data stays inaccessible and protected at all times. Compliance is enforced by market-leading privacy technologies built into our easy-to-use data collaboration platform.
Raw data is encrypted and inaccessible
The clean room encrypts the data at source with a key that only the data custodian can access. Only they can see the raw data — not even Decentriq or the cloud provider.
Explore the dataset with synthetic data
Generate a synthetic dataset based on the original data. This gives analysts the freedom to explore while personally identifiable information stays protected.
Approve and run analysis
Write Python, R, or SQL scripts and submit them for approval to run on restricted data. The dataset itself remains confidential and encrypted throughout.
Access results
Get access to the results of approved computations. Discover new insights to detect diseases early, inform treatment pathways, and develop new healthcare innovations.
Understand the full patient journey and measure outcomes
Especially for complex diagnoses, patients interact with multiple healthcare providers, pharmacies, and often devices and apps. In Decentriq, these data sources can be combined and analyzed — without revealing sensitive information — to create a full picture of treatment and outcomes. This can, for example, be used to assess the efficacy of new medications or digital interventions for reimbursement.
Discover and assess novel biomarkers without sharing patient sample data
To discover and develop novel biomarkers — or to validate it for use in a new population — pharmaceutical companies need to be able to test their biomarker models on very large patient datasets. In Decentriq, they can run this analysis on multi-site and multi-modal data without directly accessing patient data. Their proprietary models remain confidential as well.
Develop and deploy tools to detect and treat diseases earlier
Diagnostics tools can support healthcare professionals immensely, especially in rare disease detection and assisting decision-making. With Decentriq, life sciences companies can train AI assistants and other complex diagnostic models on disparate patient data while both the data itself, and the model, stay completely confidential. They can also deploy these tools across sites without needing to set up new infrastructure.
Decentriq enables organizations to make use of siloed healthcare data to improve patient care in a privacy preserving manner. Our platform provides you and your partners with a flexible infrastructure to analyze and generate results without the need to ever share any data and bring better and faster scientific innovation to patients.
Decentriq's data clean rooms facilitate a wide range of collaboration scenarios, from small-scale one-to-one partnerships to collaborative ecosystems involving multiple partners working together on data. Read more about how Roche is using Decentriq in our blog post.
Whether your aim is to identify novel biomarkers on the basis of sensitive data, to analyze data from multiple sites and partners, or to train sensitive AI/ML models, our data clean rooms enable flexible, privacy-preserving analytics while decreasing overhead costs.
Do you have a specific use case in mind you would like to explore? Get in touch with us.
Most methods for sharing data involve just that — sharing your data and hoping it is in good hands or that it is used according to your wishes. Decentriq enables meaningful insight generation from collaborations, without data ever having to be shared. We also guarantee that no one — not even Decentriq — can see the raw data you’re collaborating on.
Using confidential computing combined with synthetic data generation, output privacy measures and strict data access controls, our platform enforces GDPR-compliant analysis, and provides proof in the form of an immutable audit log. For more information on why you need more than standard encryption, read our blog post: Standard encryption is not all you need.
The Decentriq platform does not require any additional or special infrastructure from any users and supports seamless integrations with currently existing workflows.
On the data custodian side, files just need to be connected to the platform and they will be encrypted at source. And computations need to be approved in line with the purpose of the collaboration.
Data analysts can freely define code using supported languages (SQL, R, or Python) in the data clean room. This enables them to conduct sophisticated modeling and data science tasks on datasets while ensuring privacy and security.
Privacy-enhancing technologies can be divided into two segments: Input privacy and output privacy. At Decentriq, we do not believe in a single solution for privacy technology, which is why our data clean rooms employ multiple input and output privacy enhancing technologies such as confidential computing, K-anonymity (aggression), differential privacy, and synthetic data.
Data encryption at rest and in transit have been the standard for decades. However, this still leaves data vulnerable while it is in use (being analyzed). Decentriq leverages confidential computing technology to ensure your data remains encrypted in use, making it the first analytics platform that encrypts your data throughout its entire lifecycle.
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See how healthcare and life sciences organizations are using Decentriq
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