Differential Privacy is a mathematical framework aimed at providing privacy guarantees when analyzing and sharing data. It ensures that the inclusion or exclusion of a single individual's data does not significantly affect the outcome of any analysis, thus protecting their personal information. This is achieved by adding controlled noise to the data or the results of queries, making it difficult to identify individual data points. Common use cases include statistical databases, machine learning models, and data sharing initiatives where privacy is paramount, such as healthcare and finance.
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AI Fundamentals