Privacy-preserving matching of community-contributed content

Abstract

Popular consumer review sites, such as Yelp and Tripadvisor, are based upon massive amounts of voluntarily contributed content. Sharing of data among different review sites can offer certain benefits, such as more customized service and better-targeted advertisements. However, business, legal and ethical issues prevent review site providers from sharing data in bulk. This paper investigates how two parties can privately compare their review datasets. It presents a technique for two parties to determine which (or how many) users have contributed to both review sites. This is achieved based only upon review content, rather than personally identifying information (PII). The proposed technique relies on extracting certain key features from textual reviews, while the privacy-preserving user matching protocol is built using additively homomorphic encryption and garbled circuit evaluation. Experimental evaluation shows that the proposed technique offers highly accurate results with reasonable performance. © 2013 Springer-Verlag.