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Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats - Softcover
Synopsis
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 Features
- Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches
- Develop and deploy privacy-preserving ML pipelines using open-source frameworks
- Gain insights into confidential computing and its role in countering memory-based data attacks
- Purchase of the print or Kindle book includes...
About the Author
Srinivas Rao Aravilli has 25 years of experience in research and development of software products across various domains (search, ML/AI, distributed computing, privacy, and security). He is a speaker in several technical conferences related to Responsible AI, AIOps, Privacy Engineering, and distributed computing/processing. He published research papers in various journals related to Apache spark, SGX enclaves, SoA, ML/AI. Srinivas graduated with a master's degree in computer applications...
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- PublisherPackt Publishing
- Publication date2024
- ISBN 10 1800564678
- ISBN 13 9781800564671
- BindingPaperback
- LanguageEnglish
- Number of pages402
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