PRIVACY-BY-DESIGN ANALYTICS & AI

The future-proof way of leveraging sensitive data for analytics and AI

The sarus vault allows secure access

Sarus is the only solution that combines computation on the original data and generation of synthetic data — powered by Differential Privacy.

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SARUS GATEWAY

Privacy-first data access

"Data Cannot be Fully Anonymized and Remain Useful"
(Cynthia Dwork, Godel prize and inventor of Differential Privacy).

From there, the most efficient way to achieve both high utility and strong privacy is to compute on non-anonymized data with protection on computation output. Data practitioners benefit from the full data utility without comprising on privacy.

The Sarus Gateway is the fruit of this  vision.

Deployed Anywhere

Whether on-premises or in public clouds, Sarus deploys easily through containerization, and scales smoothly by running on Kubernetes.

Inherently more secure

Sarus inherits all security properties of the original infrastructure and avoids moving data outside externally. All interactions with sensitive data have to go through the Gateway.

Leverage data in full fidelity

No matter how sensitive the input data may be the output will be provably safe. Practitioners leverage the full fidelity of their data assets instead of truncated, redacted, or synthetic versions.

FUTURE-PROOF COMPLIANCE

Finest control over data access & full auditing capabilities

Dashboard

Next gen access control for sensitive data
Manage who can access which dataset and what they can do with it with unprecedented precision. Define privacy policies that can be deployed universally irrespective of data sensitivity, user trust, or learning objectives.

Scaling policies with mathematical privacy
Privacy policies should not be guesswork. Instead, use the mathematical framework of differential privacy to have a quantitative and replicable approach to risk management.

Full logging and auditing trail
Each access and each query goes through a gatekeeper that enforces all privacy settings. Every interaction with sensitive information is logged and available for reporting and auditing.

Dashboard screenshot
FUTURE-PROOF COMPLIANCE

Finest control over data access & full auditing capabilities

Next gen access control for sensitive data
Data access used to be all or nothing: very few users would get full access, others get nothing. With Sarus, t's easy to find the right level for all users and situations based on objective privacy goals.

Scaling policies with mathematical privacy
Privacy policies should not be guesswork. Instead, use the mathematical framework of differential privacy to have a quantitative and replicable approach to risk management.

Full logging and auditing trail
Each access and each query goes through a gatekeeper that enforces all privacy settings. Every interaction with sensitive information is logged and available for reporting and auditing.

SYNTHETIC DATA

High fidelity synthetic samples

Synthetic CT scans

Why synthetic data when the original data can be queried?
Maximum accuracy can only be achieved using the original data. Yet, seeing individual rows is very convenient to prepare analyses, design or debug ML models, use data in external code, or even just to get a feel of data. Sarus high utility synthetic data makes it seamless.

Available by default, private by design
The Gateway natively provides synthetic data for all datasets in a fully automated way. This data comes with the mathematical protections of differential privacy.

High quality all the time
The Sarus synthetic data generator beats the state-of-the-art of data generation while adapting to any data structure (tabular, text, images, series of transactions). For more on the architecture that supports our synthetic data modelling, check out our paper.

Synthetic CT-scans
FULL DATA SCIENCE CONNECTIVITY

Built for all data science workflows

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PythonKerasTensorflowTableauTableauDataiku
SnowflakeGoogle Cloud PlatformRedshift

Use any data source
Connect any data source to your Sarus Gateway and make them instantly accessible for analytics and AI applications. It is compatible with tabular data, relational data, time-series, images, text, and more in most common formats.

Compatible with all main data environments and libraries
Sarus supports most data science use cases natively. It leverages existing execution engines (spark clusters, BigQuery, Synapse-SQL, Redshift...) or provides its own. The engines can be leveraged seamlessly from the most common data science environments and ML and BI libraries. The Sarus built-in SDK makes it easy to integrate  remote data seamlessly into your existing workflows.

HOW IT WORKS

Analytics & AI on sensitive data from Day 1

1
Select data source

Select the data source to list on Sarus. Data types include numerical, categorical, series of events, images, and text. Common data storage and formats are supported.

2
Define user access policies

Define the rules governing the data practitioners’ access. Rule templates implement compliance best practices and can be fine tuned to your needs.

3
Use in your data workflows

Data practitioners connect to the Gateway from their favorite environment (python, SQL, etc.) and interact seamlessly with the remote data.

Ready to put all of your data to work?

Get in touch, you'll be up and running in no time.
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