Imagine that an organisation stores a vast amount of data in a safe. The information is secure, but to produce any summary reports of the data or to query that data, the data must be removed from the safe for analysis. At this point of analysis, all of the raw sensitive data is exposed. Now imagine that pre-defined queries and macro analysis could take place on the data without the safe ever being opened. Further still, data in multiple safe environments, across multiple organisations (in multiple countries), could be analysed collectively, by consent of the data owners, without any raw sensitive data leaving their safe environments. Queries that yield no sensitive information can be executed on sensitive data, held within the safe environments, without any sensitive information being disclosed at any point. This is the promise of privacy preserving analysis, using privacy enhancing technologies (PETs).
The paper includes ten case studies of current innovation, pilots and projects of privacy preserving analysis related to anti-money laundering (AML) and financial crime detection use-cases. These case studies demonstrate how financial institutions are exploring advances in this field of cryptographic technology to enable analysis of data from across multiple participating organisations to inform financial crime risk awareness, without the need for those organisations to share underlying sensitive data.