seniorMLOps
What is differential privacy in ML systems?
Updated May 17, 2026
Short answer
Differential privacy adds controlled noise to protect individual data contributions.
Deep explanation
It ensures that model outputs do not reveal whether a specific individual's data was included. It is achieved via noise injection techniques like Laplace or Gaussian mechanisms. It is widely used in sensitive domains.
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