A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning

Data, especially private data, has become increasingly valuable in the modern era. From hospital records to personal search histories, the increased collection and use of private data means that data analysis conducted on these data sets must protect sensitive information about individuals. Without this protection, a leak of sensitive information could easily have lasting consequences for an individual, even from seemingly innocuous data like a photo. The rise of machine learning has exacerbated these concerns even further due to its need for specific and abundant data to produce accurate predictions. This large amount of required data and machine learning models' tendency to memorize specific yet unnecessary information, such as specific IP addresses during text responses, makes private machine learning especially important[1].