Keep sensitive user data safe and secure, without sacrificing theaccuracy of your machine learning models. In Privacy Preserving Machine Learning , you will learn: Differential privacy techniques and their application insupervised learning Privacy for frequency or mean estimation, Naive Bayes classifier, and deep learning Designing and applying compressive privacy for machine learning Privacy-preserving synthetic data generation approaches Privacy-enhancing technologies for data mining and database ...
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Keep sensitive user data safe and secure, without sacrificing theaccuracy of your machine learning models. In Privacy Preserving Machine Learning , you will learn: Differential privacy techniques and their application insupervised learning Privacy for frequency or mean estimation, Naive Bayes classifier, and deep learning Designing and applying compressive privacy for machine learning Privacy-preserving synthetic data generation approaches Privacy-enhancing technologies for data mining and database applications Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You'll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels and seniorities will benefit from incorporating these privacy-preserving practices into their model development.
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