Differential Privacy is a privacy-preserving concept in data analysis. It aims to provide accurate insights from aggregated data without disclosing individual contributions. It introduces randomness to the data or results to maintain anonymity. Differential Privacy is vital in scenarios where data confidentiality is paramount, such as healthcare or census data, safeguarding individual privacy while enabling valuable insights from large datasets.
