Preserving privacy of fraud detection rule sharing using Intel's SGX

The collaboration of financial institutes against fraudsters is a promising path for reducing resource investments and increasing coverage. Yet, such collaboration is held back by two somewhat conflicting challenges: effective knowledge sharing and limiting leakage of private information. While the censorship of private information is likely to reduce knowledge sharing effectiveness, the generalization of private information to a desired degree can potentially allow, on one hand, to limit the leakage, and on the other hand, to reveal some properties of the private information that can be beneficial for sharing. In this demo we present a system that allows knowledge sharing via effective adaptation of fraud detection rules while preserving privacy. The system uses taxonomies to generalize concrete values appearing in fraud detection rules to higher level concepts which conform to some privacy/utility requirements set by the owner. Our demonstration will engage the CIKM'18 audience by showing that private information can be abstracted to enforce privacy while maintaining its usage by (partially) trusted allies.