Understanding privacy-preserving matching techniques for data analytics

In the wake of years of high-profile data leaks, regulatory tidal waves like GDPR and ever-mounting pressure from media and consumers, data leaders are exploring new methods to protect customer personal data throughout the enterprise analytics lifecycle.

This webinar explores the emergence of privacy-preservation technologies in enterprise analytics, how they can be most effectively applied in analytics projects and what data leaders need to consider when protecting customer privacy.

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Data Republic’s Daniel Austin provides an overview of privacy-preserving matching techniques:

  • The changing role of data matching in enterprise analytics & digital service delivery
  • The pros and cons of common methods of privacy protection in data preparation or analytics
  • The emerging field of privacy-preservation technologies and their application in data analytics
  • What to consider when evaluating privacy-preservation methods/ options for specific data & analytics projects
Data Republic Dan Austin

Dan Austin

Daniel is the Technical Product Manager for Senate Matching, Data Republic’s unique privacy-preserving matching technology. Joining Data Republic in 2015 as a founding team member, Dan leads product strategy and development for Data Republic’s patent-pending Senate Matching technology suite. With an extensive career working at the intersection of data, product development and emerging technologies Dan has previously held roles at Westfield, UTS and Smart Services.


Why Data Republic?

With the value of data climbing exponentially and data laws intensifying rapidly, the need for intelligent data tools cannot be overstated. Data Republic’s simple to use platforms offer connection to a global ecosystem of data talent, multi-use legal frameworks and safe data-matching between organizations. Revolutionize the way your data performs by joining Data Republic.

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