Products ensure privacy is intrinsic to modern BI

Privitar products allow organisations to analyse datasets containing sensitive information (e.g. customer data, employee records, banking transactions, trade data) while preserving privacy or confidentiality. This opens up data for safe secondary use while ensuring consistent and accountable protection of private information.


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 senstive 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 intutitive 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 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

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 also 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 case
  • Watermarking within masking and noise addition
  • Clear management and audit of all data sharing
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Privitar Lens™ is a privacy-preserving query interface well suited for reporting and statistical analysis. Privitar Lens allows analysts to perform sophisticated analytical queries of the data (e.g. counts, sums, histograms), but prevents direct access to the underlying sensitive data. 

Usage is subject to strict authentication and access control, and all queries are logged and audited. It allows data holders to post tables with their desired privacy controls for each analyst and for each column. Privacy controls are then dynamically applied to each query submitted. 

Privitar Lens uses noise addition to prevent users from extracting private data, ensuring differential privacy. Further protection comes from behaviourial analytics for attack detection. 

Privitar Lens tracks each query and calculates the total privacy risk across queries. It stops releasing new results after the privacy risk reaches a pre-defined threshold.

Privitar Lens has a number of features which make the product easy to use for non-privacy experts, including an intuititive web interface and REST API. It connects to data stores such as PostgreSQL or Hive with an extensible set of connector modules. Privitar Lens is installed on-premise or in the cloud. 

Interactive analysis

  • Perform interactive analytics and data science without direct access to raw data
  • Execute custom code in a privacy-preserving sandbox
  • Conduct behavioural analytics and threat modelling

Creation of data products

  • Controlled querying of sensitive data: enable safe access without disseminating the dataset
  • Create structured and dynamic data products
  • All queries are audited for complete visibility of data access

Essential to modern data architectures

Like security, privacy protection should be designed into data systems at every level, not bolted on as an afterthought. Privitar products add foundational privacy to your data processing architecture at every stage: ingest, data preparation, analytics, statistical modelling and reporting.

  • Integrate with and execute within your Hadoop environment and scale to big data, so there is no need to take data out of its secure environment
  • Bring consistency, auditability and accountability to the protection of sensitive data, through the use of centrally managed Privacy Policies
  • Integrate with metadata stores both to identify sensitive data and to record how risks have been mitigated
  • Record full data lineage of anonymised and shared data


Companies may be unknowingly and unwillingly leaking sensitive information in non-obvious ways as the use of data for secondary purposes expands. Privitar is providing products that integrate neatly with Cloudera Enterprise, the leading open source data management platform based on Apache Hadoop.

Tim Stevens, VP Corporate and Business Development, Cloudera

Privacy matters: One solution, on the privacy side at least, is to separate the identity of the person being measured by a sensor from the data they generate. John Taysom, a fellow of the University of Cambridge and co-founder of privacy company Privitar, believes this “disassociation” is key because companies and governments get the data without a risk to privacy.

Sean Hargrave, The Guardian

It is essential that we develop practical ways of protecting privacy, otherwise we may not be able to sustain growth in the internet based economy.

David Cleevely CBE, FREng:
Chairman Centre for Science & Policy, University of Cambridge

Privacy is a game changer; it will be to organizations in 2016 what websites were to companies in 2000. So this is the year to up the ante on your investments: you need the right cross-functional team, good governance practices, and the technical tools to ensure that all of your systems are in compliance with both laws and internal privacy guidelines. Making the right investments will let your firm drive business growth, win new customers, and build deeper customer relationships.

Forrester Research, Jan ‘16

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