Privacy-preserving ML

Privacy-preserving ML is a hot topic since it proposes solutions for privacy issues in the ML field. Privacy-preserving ML proposes methods that allow researchers to use sensitive data to train ML models while withholding sensitive information from being shared or accessed by a third party or revealed by the ML model. In the first subsection, we will examine common methods for mitigating privacy issues in datasets.

Approaches for privacy-preserving datasets

In this section, we’ll delve into standard approaches for handling and protecting sensitive information in datasets. We will look at anonymization, centralized data, and differential privacy.

Anonymization

Anonymization can be considered one of the earliest approaches for privacy issues in datasets. Let’s assume you are given a dataset of patients’ medical records that contains their addresses, phone numbers, and postal codes. To anonymize this dataset, you can simply remove...