Gradual Differentially Private Programming.

The article discusses the importance of protecting sensitive information in large datasets and the limitations of de-identification techniques, using the example of Chilean Electoral Service data being re-identified. Differential Privacy (DP) is introduced as a robust solution to privacy concerns, ensuring that adding or removing an individual's data has a negligible impact on the outcome. The article highlights the development of programming languages and type systems, like Fuzz and Jazz, which help achieve DP by incorporating mechanisms to handle sensitivity and noise addition, with a focus on gradual typing to ease the adoption of these techniques.