Privacy-Preserving Machine Learning
portes grátis
Privacy-Preserving Machine Learning
A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
Aravilli, Srinivas Rao; Hamilton, Sam
Packt Publishing Limited
05/2024
402
Mole
Inglês
9781800564671
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Table of Contents
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models
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Homomorphic Encryption, Privacy Preserving, Machine Learning, Secure Multiparty Computation, data privacy, Secure Multiparty Computation, inference privacy
Table of Contents
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
