Privacy-first data processing powered by Differential Privacy

Apply the latest research on privacy-preserving data processing directly into your data architecture.

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Native to modern data infrastructures

Sarus blends into existing data infrastructures and workflows, both on-premises or in public clouds. Sarus follows the latest standards for deployment and resource management (Docker, Kubernetes). Data is available to use in the most common data science environments and tools (PowerBI, Tableau) or with the most used python libraries.


All interactions with sensitive data go through the Gateway's API. The information that comes our of the gateway is protected with differential privacy. 


Differential Privacy at the core

Data protection now has a universal standard: Differential Privacy. No other privacy definition provides satisfying protection against  re-identification using additional information (e.g.: using public datasets, overlapping requests...). Yet, privacy regulations make it clear that such a protection is a prerequisite for anonymous information. For instance, GDPR recital 26 specifies that “data which could be attributed to a natural person by the use of additional information should be considered to be information on an identifiable natural person".

At Sarus, Differential Privacy is at the core of all interactions with sensitive data. Whether it is metadata, synthetic data, SQL analyses, or machine learning models, all output from Sarus is measured and controlled using the  principles of differential privacy.

Probability density of the output of a differentially-private mechanism

Sarus builds on and extends the most robust implementations of differential-privacy. It bundles them into a unified API and provides exhaustive accounting of the output privacy risk. This allows scalable data compliance irrespective of the data practitioner's objectives. Only Sarus provides such a native guarantee against attacks with additional information.

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