Privacy by Design

Unlock maximum value from sensitive data

Sarus empowers enterprises to leverage their most sensitive data assets for analytics and AI applications. With Sarus, AI scientists and analysts extract the full value from any sensitive data using the highest level of data protection — differential privacy.
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Why Sarus

Accelerate Data Innovation and Unlock Collaboration Opportunities

Building new applications using sensitive data can require lengthy approvals processes and anonymization techniques that dilute the value of the data.  Many projects never advance because the data is deemed too sensitive to work on. 

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Accelerate projects by streamlining compliance

Jumpstart data projects and empower data scientists to explore new data applications without delays.

Collaborate on data

Data practitioners can leverage remote data assets – unlocking collaboration opportunities across business lines and national borders or with external partners.

Achieve more powerful learning using ALL data

Build and train AI models on the original data – without destroying valuable information through anonymization. Full fidelity and structure of data is preserved.

Minimize data security risks

Sensitive data is never copied or revealed to data practitioners. All insights and models are protected with the highest privacy standards.

How it Works

Privacy-Preserving Learning Built into Every Data Project

Empower data teams to promptly deliver machine learning models or BI analysis on any dataset with an improved data science workflow.

Data stays in its secure infrastructure

Works within the original data infrastructure and inherits security practices. Data owners define access rights and privacy policies for each dataset. Data is never copied or moved.

Learn on all types of data

Data owners can make any dataset available for innovation. Privacy guarantees are maintained irrespective of the type of data, including where data-masking techniques fall short like on complex data structures or high dimensional data.

Designed for Data Scientists

The Sarus API integrates seamlessly with data scientists’ current workflows allowing them to build models on remote data exactly like they would on local data – using their preferred models or libraries.

Differential Privacy in every computation

Differential privacy is built-into every interaction between the data scientist and the data set. Re-identification is impossible in everything the data practitioner sees, from the top level dataset descriptions to the final models or insights.

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From Our Blog

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Full privacy-preserving AI is becoming possible thanks to new technologies and theoretical frameworks. This is how Sarus achieves it.

Introduction to Privacy-preserving AI and analytics

Full privacy-preserving AI is becoming possible thanks to new technologies and theoretical frameworks. This is how Sarus achieves it.
Modern privacy-preserving learning techniques offer both better results and higher data protection over the legacy data-masking approach.

Overcoming the Limitations of Data-Masking

Modern privacy-preserving learning techniques offer both better results and higher data protection over the legacy data-masking approach.