Privitar Publisher takes sensitive data and applies a privacy policy to create an anonymised copy which can safely be used for investigative analytics, machine learning, and sharing with trusted parties.

Privitar Publisher tokenises or encrypts identifying fields in a dataset, and then perturbs the rest of the data to prevent re-identification via linkage attacks. This statistical perturbation mitigates risk while preserving the analytical utility of the dataset. Centralised privacy policy management ensures consistency, accountability and traceability, with clear recording of data lineage and auditing of usage.

Privitar Publisher generalises data to be resistant to linkage attacks whilst defending against the disclosure of an individual's sensitive attributes. Multiple quasi-identifiers are generalised to achieve k-anonymity, in a way that automatically minimises distortion and so maximises the utility of the data for analytics and secondary use.

Taking a policy-oriented approach, Privitar Publisher's intuitive web interface allows you to centrally manage and action privacy policies with no need for scripting or custom coding. Policies are executed within an existing compute cluster enabling high performance and scalability. Privitar Publisher can be installed on-premise or in the cloud. 

Publisher data sharing:
Data sharing outside the organisation

When sharing data outside your organisation, you lose control of how it might be accessed and further shared. This should be mitigated through contractual agreements, and by using privacy technology to reduce the risk of data disclosure. Privitar Publisher allows you to:

  • When data is shared, relinquish control of subsequent access

  • Protect against re-identification through linkage attacks via k-anonymity and attribute disclosure via l-diversity

  • Optimise data utility for specific use cases

  • Watermarking within masking and noise addition

  • Clear management and audit of all data sharing

Publisher data masking:
Safe data use within organisations

  • Anonymise sensitive data and create safe copies for analytics, development and test

  • Preserve useful patterns in data: Retain structure, format and relationships

  • Clear management and audit of all data anonymisation

  • Policy-driven, to ensure consistency and repeatability







Learn More or Request a Demo



Join us on social media