Private and Secure Fuzzy Name Matching w/ Harsh Kasyap and Ugur Atmaca

Abstract

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. Due to regulatory restrictions 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. It has been observed that the names of account holders may be recorded differently in each data set. A novel privacy-preserving approach for fuzzy name matching across institutions is proposed, employing fully homomorphic encryption with locality-sensitive hashing. The efficiency of the approach is enhanced using a clustering mechanism.

About the speakers

Harsh and Ugur are post-doctoral research associates in the Alan Turing Institute London. Their research interests include private data sharing and privacy-preserving machine learning.

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