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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

4 Local differential privacy for machine learning

This chapter covers

  • Local differential privacy (LDP)
  • Implementing the randomized response mechanism for LDP
  • LDP mechanisms for one-dimensional data frequency estimation
  • Implementing and experimenting with different LDP mechanisms for one-dimensional data

In the previous two chapters we discussed centralized differential privacy (DP), where there is a trusted data curator who collects data from individuals and applies different techniques to obtain differentially private statistics about the population. Then the curator publishes privacy-preserving statistics about this population. However, these techniques are unsuitable when individuals do not completely trust the data curator. Hence, various ...

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Publisher Resources

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