Differential Privacy

In data science and machine learning, privacy concerns are of utmost importance when working with sensitive data. Epsilon-Delta Differential Privacy (EDDP) is a rigorous framework that provides strong privacy guarantees for individuals whose data is being analysed. It ensures that the results of computations performed on sensitive data do not reveal specific information about any individual participant.

EDDP is based on the concept of adding controlled noise to computations in order to protect privacy. The framework introduces two key parameters: epsilon ($\epsilon$) and delta ($\delta$). Epsilon controls the level of privacy protection provided, while delta represents the probability of privacy violation.

The key equations in EDDP are as follows:

Epsilon ($\epsilon$): It quantifies the privacy loss by determining the amount of information about an individual that can be inferred from the computed results. A smaller value of $\epsilon$ indicates a stronger privacy protection.

$$\epsilon \geq 0$$

Delta ($\delta$): It measures the probability that an adversary can distinguish between two data sets based on the computation results. A smaller delta value implies a lower chance of privacy breaches.

$$0 \geq \delta \geq 1$$

To achieve differential privacy, algorithms must satisfy the $\epsilon$-differential privacy definition. This ensures that the presence or absence of any individual's data has minimal impact on the final output. This means that if two datasets differ only by one individual's data point, the resulting computations should not reveal whether that individual's data was included or excluded.

Applying EDDP to machine learning and data science tasks involves carefully designing algorithms and mechanisms to inject controlled noise into computations, such as aggregation, data analysis, and model training. The challenge lies in balancing utility (accuracy of the results) and strong privacy guarantees.

EDDP has become a widely used privacy framework in various domains, including healthcare, finance, and social sciences. It enables organisations to leverage sensitive data while ensuring privacy protection for individuals. By incorporating EDDP into data science and machine learning workflows, practitioners can address privacy concerns and build trust with data providers, fostering responsible and ethical data-driven practices.