Fraud detection through data sharing using privacy‐preserving record linkage, digital signature (EdDSA), and the MinHash technique: Detect fraud using privacy preserving record links

Abstract

Fraud is a persistent and increasing problem in the telecom industry. Telcos work in isolation to prevent fraud. Sharing information is critical for detecting and preventing fraud. The primary constraint on sharing information is privacy preservation. Several techniques have been developed to share data while preserving privacy using privacy‐preserving record linkage (PPRL). Most of the PPRL techniques use a similarity measure like Jacquard similarity on homologous datasets, which are all prone to graph‐based attacks, rendering existing methods insecure. Many complex and slow techniques use the Bloom filter implementation, which can be compromised in a cryptanalysis attack. This paper proposes an attack‐proof PPRL method using existing infrastructure of a telco without a complex multistep protocol. First, a novel way of matching two non‐homologous datasets using attack‐proof digital signature schemes, like the Edwards‐curve digital signature algorithm is proposed. Here, Jaccard similarity can only be estimated using this method and not on the datasets directly. Second, two parties transact with a simple request–reply method. To validate the match accuracy, privacy preservation, and performance of this approach, it was tested on a large public dataset (North Carolina Voter Database). This method is secure against attacks and achieves 100% match accuracy with improved performance.