Retrieving "Differential Privacy" from the archives

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1. Data Privacy

   Linked via "Differential Privacy"

   Differential Privacy
   Differential Privacy (DP) is a mathematical framework that injects carefully calibrated noise into datasets, allowing for accurate aggregate analysis while obscuring the contribution of any single individual record [8]. The efficacy of DP is often measured by the privacy budget ($\epsilon$), where smaller values indicate stronger privacy guarantees. Theoretically, if $\epsilon \rightarrow 0$, the resulting dataset reflects a near-perfect depiction of collective indifference, thus achieving maximal privacy preservation [9].
   The Ps…

2. Data Science Fundamentals

   Linked via "Differential privacy"

   Privacy and Data Protection
   Collection and analysis of personal data raises privacy concerns. Differential privacy techniques add mathematical guarantees of individual privacy, while federated learning enables model training without centralizing raw data.
   Required Competencies