Private and Secure Fuzzy Name Matching

Modern financial institutions rely on data for many operations, including a need to drive efficiency, enhance services and prevent financial crime. Data sharing across an organisation or between institutions can facilitate rapid, evidence-based decision making, including identifying money laundering and fraud. However, data privacy regulations impose restrictions on data sharing. Privacy-enhancing technologies are being increasingly employed to allow organisations to derive shared intelligence while ensuring regulatory compliance. This paper examines the case in which regulatory restrictions mean a party cannot share data on accounts of interest with another (internal or external) party to identify people that hold an account in each dataset. We observe that the names of account holders may be recorded differently in each data set. We introduce a novel privacy-preserving approach for fuzzy name matching across institutions, employing fully homomorphic encryption with locality-sensitive hashing. The efficiency of the approach is enhanced using a clustering mechanism. The practicality and effectiveness of the proposed approach are evaluated using different datasets. Experimental results demonstrate it takes around 100 and 1000 seconds to search 1000 names from 10k and 100k names, respectively. Moreover, the proposed approach exhibits significant improvement in reducing communication overhead by 30-300 times, using clustering.