PROJECT TITLE :

Privacy-Preserving Fraud Detection Across Multiple Phone Record Databases

ABSTRACT:

Subscription fraud, i.e., customers signing up to a service with no intent to pay, causes important losses within the telecommunication industry. Telecom operators have developed strategies to spot those fraudsters, however fraudsters tend to migrate from one carrier to another. Knowledge sharing between telecoms would increase fraud detection rates, but phone records are protected by law and telecom operators may be reluctant to share info regarding fraudsters as a result of they see it as giving a competitive advantage. We tend to propose several protocols to enable fraud detection across multiple databases without revealing further data. We tend to also propose a model to get phone records, with that we have a tendency to evaluate how the selection of parameters affects detection performance. We have a tendency to show feasibility, performance and costs with implementations of our protocols.