Advanced Data Privacy: Gretel Privacy Filters and ML Accuracy

The use of Gretel's Privacy Filters on synthetic datasets can impact machine learning accuracy in various ways. These filters prevent the creation of synthetic data with weaknesses commonly exploited by adversarials, thus enhancing privacy protection. Experiments conducted using these filters showed that synthetic data average accuracy is usually not far from original data's average accuracy. When the Privacy Filters are set to "med", the accuracy remains similar and sometimes even exceeds the original data accuracy. When set to "high", the results are variable but still quite good, with a modest hit on accuracy in some cases, no impact in others, and sometimes an improvement in accuracy. The tradeoff between privacy and accuracy is complex, as making data private can lead to more people sharing their data and being more honest about it, which also impacts accuracy.