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A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Privacy-Preserving Machine Learning

<span xml:lang="en">Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats</span>

Aravilli, Srinivasa Rao

Hamilton, Sam

Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key FeaturesUnderstand machine learning privacy risks and employ machine learning algorithms to safeguard data against breachesDevelop and deploy privacy-preserving ML pipelines using open-source frameworksGain insights into confidential computing and its role in countering memory-based data attacksPurchase of the print or Kindle book includes a free PDF eBookBook Description– In an era of...

Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and...

Format

Privacy-Preserving Machine Learning

A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Srinivasa Rao Aravilli

Sam Hamilton

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