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Electronic information is increasingly often shared among entities withoutcomplete mutual trust. To address related security and privacy issues, a fewcryptographic techniques have emerged that support privacy-preservinginformation sharing and retrieval. One interesting open problem in this contextinvolves two parties that need to assess the similarity of their datasets, butare reluctant to disclose their actual content. This paper presents anefficient and provably-secure construction supporting the privacy-preservingevaluation of sample set similarity, where similarity is measured as theJaccard index. We present two protocols: the first securely computes the(Jaccard) similarity of two sets, and the second approximates it, using MinHashtechniques, with lower complexities. We show that our novel protocols areattractive in many compelling applications, including document/multimediasimilarity, biometric authentication, and genetic tests. In the process, wedemonstrate that our constructions are appreciably more efficient than priorwork.

EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity