Solving privacy issues with synthetic data
In certain fields, such as healthcare and finance, a lot of data is available, but the main obstacle is annotating and sharing the data. Even if we have a large-scale real dataset that is “perfectly” annotated, sometimes, we cannot share it with ML practitioners because it contains sensitive information that could be used by a third party to identify individuals or reveal critical information about businesses and organizations.
As we know, ML models cannot work without data, so what is the solution? A simple solution is to use the real data to generate synthetic data that we can share with others without any privacy issues while still representing the real data. We can utilize some synthetic data generation approaches to leverage the real dataset to generate a synthetic dataset that still represents the relationship between variables, hidden patterns, and associations in the real data while not revealing sensitive information...
