Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems. (arXiv:2401.02450v1 [cs.CR])

Global financial crime activity is driving demand for machine learning
solutions in fraud prevention. However, prevention systems are commonly
serviced to financial institutions in isolation, and few provisions exist for
data sharing due to fears of unintentional leaks and adversarial attacks.
Collaborative learning advances in finance are rare, and it is hard to find
real-world insights derived from privacy-preserving data processing systems. In
this paper, we present a collaborative deep learning framework for fraud
prevention, designed from a privacy standpoint, …

adversarial adversarial attacks attacks crime data data sharing demand distributed driving finance financial financial crime financial institutions fraud fraud prevention global institutions isolation leaks locally machine machine learning prevention private sharing solutions systems unintentional