Sarus empowers enterprises to leverage their most sensitive data assets for analytics and AI applications. With Sarus privacy-first gateway, extract the full value from all data assets with the highest data protection — Differential Privacy.
Build powerful insights and AI models that use every data asset in full fidelity without watering down the data value through anonymization. Learnings always come from the original data.
Unlock internal and external collaboration opportunities by leveraging data across lines of business, organizations, and regulatory borders.
Do away with weak anonymization processes that need reassessing every time. Use mathematical protection that works in all cases and accelerate compliance by strengthening data security.
No need to build bespoke anonymization workflows each time, or to adapt your data infrastructure for each project. Sarus provides a no-code way to put your data to work directly on-premises or in SaaS.
Differential privacy provides the strongest data protection irrespective of data sensitivity or learning objectives. The mathematical protection future-proofs compliance and scales up processes.
Starting ML, BI, or analytics project has never been easier. Leverage every type of data in full and without watering it down with anonymization, from the comfort of the tools you are used to (Python, TensorFlow, SQL…).
Get the finest control over data access by leveraging the latest privacy research.
Automated generation of high utility samples for preparatory work and high level analyses.
Use Sarus Privacy-first Gateway to interact with the original data asset in privacy-preserving manner.
A full suite of differentially-private libraries and integrations that blend seamlessly into existing workflows.
A practical guide to doing privacy-preserving data science.
There is a difference between “anonymous unless you’re unlucky” and “mathematically anonymous”. Make sure to pick the one you need.
Sarus builds tools to help compute statistics, analytics or AI models from sensitive data with formal guarantees of differential privacy.
Apple has adopted and further developed a technique known in the academic world as local differential privacy to do something really exciting: gain insight into what many Apple users are doing, while helping to preserve the privacy of individual users.
Differential privacy simultaneously enables researchers and analysts to extract useful insights from datasets containing personal information and offers stronger privacy protections.
2020 US Census results will be protected using differential privacy, the new gold standard in data privacy protection.