Extract value from your sensitive data by adopting a consistent approach to privacy management through pattern-preserving data de-identification.
State-of-the-art privacy engineering techniques
Maximise both privacy and utility of your datasets, consistently and across multiple environments, with state-of-the-art and repeatable privacy engineering techniques, including data masking, automated statistical generalisation (e.g. k-anonymity) and format-preserving tokenisation.
Engineered for Big Data
Running on premise, cloud, multi-cloud and hybrid environments, Privitar Publisher’s algorithms are designed to be highly scalable, and can be executed on a variety of processing engines, such as Hadoop clusters or streaming data flow platforms, to process data at rest and in motion.
Privacy governance simplified
The easy to use management interface allows non-technical users to author, review and manage data protection policies. By integrating with major enterprise metadata catalogues Privitar Publisher enables the tracking of data lineage, the recording the history of data transformations, and automation of access control and data lifecycle management.
Data risk management
Protected Data Domains (PDDs) are containers for data releases that reduce your risk exposure and enable secure data usage that scales across your organisation.
Watermarking: secure data releases
Privitar Publisher embeds invisible watermarks into protected data. In the event of unauthorised distribution, the watermark allows the origin and lineage of a file to be established.
This acts as a deterrent against such unauthorised behaviour.
Built for the needs of large organisations, Privitar Publisher includes auditing, multi-environment support, Active Directory integration for authentication and management of user roles, Kerberos security integration and user impersonation, and the ability to automate workflows via REST APIs.