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This E-Book covers

  • The challenges facing the analytics leader when considering the data utility vs. data privacy tradeoff
  • Use cases that can be enabled if this trade-off is optimized
  • Privacy-preserving machine learning — what it is and how it is done
  • A primer on differential privacy, a quantifiable approach to unlocking value from sensitive data

Data science organizations play a central role in today’s data-driven economy.  The growth of the application of data science techniques in recent years has been a product of advances in computing and the availability of rich datasets.  One of the central questions facing organizations is how to satisfy regulatory requirements, implement information security and data protection protocols, meet their contractual obligations, and ensure the responsible use of information, all without restricting the pace of innovation for the data science teams.

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