Fraud detection is a critical task, but detecting fraud can be challenging due to class imbalance. Furthermore, the availability of the data is also limited, because the data is very sensitive and cannot be shared between financial institutions due to privacy regulations. One way to address this is by applying differential privacy. Differential privacy is a mechanism that applies controlled noise to the data to achieve a certain privacy guarantee. However the added noise may also affects the utility of the data.
Privacy amplification techniques, such as subsampling, have been introduced to increase the privacy guarantee without directly adding additional noise. This paper investigates how privacy amplification by subsampling affects the privacy-utility tradeoff in differentially private fraud detection.
To answer this question, an experiment is performed in which logistic regression models are trained using different subsampling rates and privacy budgets.
The results show that subsampling can improve the performance of a model in highly private settings. However, this improvement is primarily in distinguishing between fraudulent and legitimate transactions, rather than detecting more fraudulent transactions. Therefore, whether subsampling is effective depends on the application and the costs of false positives and false negatives. ... Read more