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Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivas Rao Aravilli, Sam Hamilton - okladka książki

Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivas Rao Aravilli, Sam Hamilton - okladka książki

Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivas Rao Aravilli, Sam Hamilton - audiobook MP3

Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivas Rao Aravilli, Sam Hamilton - audiobook CD

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Stron:
402
Dostępne formaty:
     PDF
     ePub

Ebook (92,88 zł najniższa cena z 30 dni)

129,00 zł (-28%)
92,88 zł

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Do przechowalni

Privacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This may seem quite challenging considering the large volume of data involved and lack of in-depth expertise in privacy-preserving machine learning.
This book delves into data privacy, machine learning privacy threats, and real-world cases of privacy-preserving machine learning, as well as open-source frameworks for implementation. You’ll be guided through developing anti-money laundering solutions via federated learning and differential privacy. Dedicated sections also address data in-memory attacks and strategies for safeguarding data and ML models. The book concludes by discussing the necessity of confidential computation, privacy-preserving machine learning benchmarks, and cutting-edge research.
By the end of this machine learning book, you’ll be well-versed in privacy-preserving machine learning and know how to effectively protect data from threats and attacks in the real world.

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O autorze książki

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 from Andhra University in 1997. His work history includes the likes of Cisco, Hewlett Packard, BEA, Interwoven. He resides in Bangalore with his wife and two children. Currently he is working as a director, data and AI Platform in Visa.

Packt Publishing - inne książki

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