Privacy-Preserving Machine Learning - J Morris Chang,Di Zhuang,G Dumindu Samaraweera
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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 synthe ... Full description
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Description
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.
More Information
| Author | J Morris Chang, Di Zhuang, G Dumindu Samaraweera |
|---|---|
| Publisher | Manning Publications |
| Release year | 2023 |
| Cover type | Softcover |
| EAN | 9781617298042 |