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In many industrial applications of big data, the Jaccard Similarity Computation has been widely used to measure the distance between two profiles or sets respectively owned by two users. Yet, one semi-honest user with unpredictable knowledge may also deduce the private or sensitive information (e.g., the existence of a single element in the original sets) of the other user via the shared similarity. In this paper, we aim at solving the privacy issues in Jaccard similarity computation witharXiv:1705.07258v1 fatcat:un7aw5etgzabbptexi5erhrppa