Privacy-preserving weighted Manhattan distance-based similarity query

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University of New Brunswick


Computing over outsourced, encrypted data in an efficient, secure and privacy-preserving way continues to be a challenge as the quantity, complexity and applications of the data continue to grow. Both data owners and data users require solutions that address the continued evolution of security and privacy needs in the presence of determined malicious actors and threats. The basic framework for similarity queries has remained relatively unchanged, but we continue to design more efficient, secure and privacy-preserving schemes that combine different encryption techniques and data structures to better address the needs of the users and changing landscape of data complexity and its attendant threats. We propose and design a similarity query scheme that uses a weighted Manhattan distance-based metric, a symmetric homomorphic encryption (SHE) technique, a kd-tree to index our data and a 2-cloud server model. Security analysis shows that our proposed scheme can achieve the desirable privacy requirements.