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 your Hadoop cluster using the Spark framework, enabling high performance and scalability. Privitar Publisher is 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