Data organizations play a central role in today’s data-driven economy.  The ability to leverage data at scale has resulted in the rapid development of advanced applications in nearly every facet of the enterprise.  The outcome is automation and predictive analytics spurring fundamental improvements of the ways organizations do business.

One of the central questions facing data 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 larger organization.  Today, chief data officers face challenging questions on how to best prepare, protect, and deploy data, balancing the need for access and protection on all data assets.

This E-Book covers

  • The challenges facing the data leader when considering the data utility vs. data privacy trade-off
  • 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

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