Eliminate case by case risk assessments and anonymization strategies. Easily implement data governance across all data assets.
Create insights, build models, and ship code without dependence on engineering or compliance approvals. Leverage the full data while still using the most common BI tools and ML libraries.
Power your existing data workflows with the gold standard of privacy instead of implementing and maintaining complex anonymization logic for every analytics and data science need.
Apply high level privacy principles across the organization so that each practitioner has the right level of access.
Do all preparatory work on high-utility synthetic data that is provided automatically for each data source.
Use Sarus Gateway to interact with original data assets in a privacy-preserving manner.
Use Sarus in your existing worklfows to connect all data sources to standard BI tools and ML libraries
Maxime Agostini, Sarus: “as organizations provide more data to analysts, the likelihood of one user being compromised is growing”
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.