FAQs
Differential Privacy Basics
What are the Core Concepts and Definitions of Differential Privacy?
FAQs
Differential Privacy Basics
What are the Core Concepts and Definitions of Differential Privacy?

The core concepts in differential privacy revolve around the idea of providing privacy guarantees through controlled random noise. Key definitions include:

- Neighboring Datasets: Two datasets are considered neighbouring if they differ by only one individual's data. This concept is central to defining how much an individual's data can influence the overall analysis.

- Randomised Mechanism: A fundamental tool in differential privacy, a randomised mechanism applies a random process (often involving noise addition) to the data or query responses, ensuring that the output does not depend significantly on any single individual's data.

- Privacy Loss Random Variable: This concept measures the change in the probability distribution of the mechanism's output when an individual's data is modified, providing a quantifiable measure of privacy risk.

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