Skip to Main Content
Privacy-Preserving Machine Learning
book

Privacy-Preserving Machine Learning

by Di Zhuang, Dumindu Samaraweera, Morris Chang
May 2023
Intermediate to advanced content levelIntermediate to advanced
336 pages
10h 3m
English
Manning Publications
Content preview from Privacy-Preserving Machine Learning

10 Putting it all together: Designing a privacy-enhanced platform (DataHub)

This chapter covers

  • Requirements for a privacy-enhanced platform for collaborative work
  • The different components of a research collaboration workspace
  • Privacy and security requirements in real-world applications
  • Integrating privacy and security technologies in a research data protection and sharing platform

In previous chapters we’ve looked at privacy-enhancing technologies that serve different purposes. For instance, in chapters 2 and 3 we looked into differential privacy (DP), which covered the idea of adding noise to data query results to ensure the individual’s privacy without disturbing the data’s original properties. In chapters 4 and 5 we looked at local differential ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Graph-Powered Machine Learning

Graph-Powered Machine Learning

Alessandro Negro
Practicing Trustworthy Machine Learning

Practicing Trustworthy Machine Learning

Yada Pruksachatkun, Matthew Mcateer, Subho Majumdar

Publisher Resources

ISBN: 9781617298042Supplemental ContentPublisher SupportOtherPublisher WebsiteSupplemental ContentPurchase Link