Data Privacy and Security in ML
Explore key techniques for data privacy and security in machine learning.
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Privacy in machine learning ensures that individual data contributions cannot be reverse-engineered, leaked, or exploited. This lesson introduces the core principles of data privacy in ML and demonstrates how to apply two foundational techniques: differential privacy and data anonymization.
Privacy preservation techniques for ML
You're building a machine learning model using user health records. Regulations like GDPR and HIPAA require that the model should not leak private information about any individual. You're tasked with applying privacy-preserving methods to protect this sensitive data during model development.
Explain the key principles of privacy preservation in machine learning and demonstrate how you would implement two fundamental techniques to protect individual data privacy.
Sample answer
This question is designed to test your understanding of privacy, security, coding skills, and awareness of privacy-utility trade-offs. Let’s look at a sample approach towards this:
Privacy preservation in machine learning is crucial for protecting individual identities while maintaining the utility of data for analysis. The two primary techniques demonstrated in this solution are:
Data anonymization
Differential privacy ...