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Machine Learning for Cybersecurity Cookbook

You're reading from   Machine Learning for Cybersecurity Cookbook Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781789614671
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Emmanuel Tsukerman Emmanuel Tsukerman
Author Profile Icon Emmanuel Tsukerman
Emmanuel Tsukerman
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Toc

Table of Contents (11) Chapters Close

Preface 1. Machine Learning for Cybersecurity FREE CHAPTER 2. Machine Learning-Based Malware Detection 3. Advanced Malware Detection 4. Machine Learning for Social Engineering 5. Penetration Testing Using Machine Learning 6. Automatic Intrusion Detection 7. Securing and Attacking Data with Machine Learning 8. Secure and Private AI 9. Other Books You May Enjoy Appendix

Private deep learning prediction

In many situations, company A might have a trained model that it wishes to offer as a service. At the same time, company A may be reluctant to share this model, so as to avoid having its intellectual property stolen. The simple solution to this problem is to have customers send their data to company A, and then receive from it predictions. However, this becomes a problem when the customer wishes to preserve the privacy of their data. To resolve this tricky situation, the company, as well as its customers, can utilize encrypted computation.

In this recipe, you will learn how to share an encrypted pre-trained deep learning model with a client and allow the client to predict using the encrypted model on their own private data.

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