Local differential privacy is a privacy-preserving technique that adds noise to individual data before it is shared or analyzed, ensuring anonymity without relying on centralized data aggregation. It is commonly used in privacy-focused analytics by companies like Apple and Google to collect user data while minimizing exposure risks.