Privacy-Preserving Deep Learning

Privacy-Preserving Deep Learning

A Comprehensive Survey

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Springer Verlag, Singapore

08/2021

94

Mole

Inglês

9789811637636

Pré-lançamento - envio 15 a 20 dias após a sua edição

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Introduction.- Definition and Classification.- Background Knowledge.- X-based Hybrid PPDL.- The Gap Between Theory and Application of X-based PPDL.- Federated Learning and Split Learning-based PPDL.- Analysis and Performance Comparison.- Attacks on DL and PPDL as the Possible Solutions.- Challenges and Future Work.
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Privacy-Preserving;Deep Learning;Machine Learning as a Service;Data Privacy;Privacy Issue on Deep Learning;Homomorphic Encryption;Secure Multi Party Computation;Differential Privacy;Secure Enclaves;Federated Learning;Split Learning