Delivering data-driven insights that fuel growth while achieving compliance for the sensitive data from your customers, partners and employees is hard, but it doesn’t have to be. With Privitar’s market-leading functionality and enterprise grade software, you can finally achieve both.
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Privitar includes the comprehensive set of privacy preserving techniques that can be applied to each of your datasets in any combination to achieve the right level of de-identification for each specific context.
Privitar Watermarks enable you to positively determine the origin, lineage and authorized users of every dataset. They encourage responsible behavior by authorized users, thereby reducing insider threats. And when you inevitably have a breach, you are prepared. Watermarks can be used to accelerate your forensic investigation and remediation so you can deliver a strong response within mandated notification requirements.
Context matters. With Privitar Protected Data Domains™ you can implement data minimization and tune the de-identification techniques according to the analysis requirements. Based on what data will be used by whom, for what purpose, and by what methods; the rules in your Privacy Policies retain data resolution where you need it while applying coarser controls where it is less vital. Whether data users are internal or external, they can utilize all of, but only the data essential to each analysis.
The Privitar Platform is designed to run natively in your on premise, cloud, multi-cloud or hybrid environment. No need to deploy agents at every data source or create a duplicate environment to de-identify your sensitive data. Whether you are protecting data at rest, in motion or on demand; Privitar consistently brings the de-identification techniques from your Privacy Policies to all of your secure, scalable environments to execute, leveraging your existing processing and security investments.
Need to safely integrate data owned by different business units, regions or even independent organizations? Privitar SecureLink™ uses blind matching and homomorphic encryption to enable distributed datasets to be securely joined while never disclosing identity-revealing linking identifiers. With SecureLink, you can generate insights from these rich, combined datasets that previously would have been out of reach.