Privacy-Preserving Machine Learning - Samaraweera, G.; Chang, J.; Zhuang, Di
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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…mehr

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Produktbeschreibung
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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Autorenporträt
J. Morris Chang is a professor in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He received his PhDfrom North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He hasled a DARPA project under the Brandeis Program, focusing on privacy-preserving computation over the internet for three years. Di Zhuang received his BSc degree in computer science and information security from Nankai University, Tianjin, China. He is currently a PhD candidate in the Department of Electrical Engineering of University of South Florida, Tampa, USA. Heconducted privacy-preserving machine learning research under the DARPA Brandeis Program from 2015 to 2018. G. Dumindu Samaraweera received his BSc degree in computer systems and networking from Curtin University, Australia, and a MSc in enterprise application development degree from Sheffield Hallam University, UK. He is currently reading for his PhD in electrical engineering at University of South Florida, Tampa.