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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

5 Advanced LDP mechanisms for machine learning

This chapter covers

  • Advanced LDP mechanisms
  • Working with naive Bayes for ML classification
  • Using LDP naive Bayes for discrete features
  • Using LDP naive Bayes for continuous features and multidimensional data
  • Designing and analyzing an LDP ML algorithm

In the previous chapter we looked into the basic concepts and definition of local differential privacy (LDP), along with its underlying mechanisms and some examples. However, most of those mechanisms are explicitly designed for one-dimensional data and frequency estimation techniques, with direct encoding, histogram encoding, unary encoding, and so on. In this chapter we will extend our discussion further and look at how we can work with multidimensional ...

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Publisher Resources

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