Detecting and Warning Abnormal Transaction of Virtual Cryptocurrency Based on Privacy Protection Framework

Abstract

For detecting and warning abnormal transaction of virtual cryptocurrency: we proposed PROTECTION (PRivacy-preserving suspiciOus Transaction detECTION), and proposed big matrix inversion algorithm to solve the problem that the physics of TEE is easily limited by memory size. Based on the privacy protection framework, we proposed three supervised learning algorithms to detect and warn abnormal transactions, they respectively are the federated logistic regression model(VERTIGO) over vertically partitioned data, the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data. According to the experimental results, we found that among the three algorithms, the federated logistic regression model(VERTIGO) over vertically partitioned data is ahead of the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data in all indicators, it has a good effect on detecting abnormal transaction of virtual cryptocurrency.