ISSN (Online) : 2456 - 0774

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ISSN (Online) 2456 - 0774

Secure k-NN Query OverEncrypted Data in KIDS


Abstract-Nearest neighbours (k-NN)query aims at identifying k nearestpoints for a given query point in a dataset. In the past few years, researchershave proposed various methods to address the security and privacy problems of k-NN query on encrypted cloud data.Now a days, various schemes have been presented to support k-NN query on encrypted cloud data.However, prior works have all assumed that the query users (QUs) are fullytrusted and know the key of the data owner (DO), which is used to encrypt anddecrypt outsourced data. We present a novel scheme for secure k-NN query on encrypted cloud datawith multiple keys, in which the DO and each QU all hold their own differentkeys, and do not share them with each other; meanwhile, the DO encrypts anddecrypts outsourced data using the key of his own. Our proposed scheme isconstructed by a distributed two trapdoors public-key cryptosystem (DT-PKC) anda set of protocols of secure two-party computation, which not only preservesthe data confidentiality and query privacy but also supports the offline dataowner. We have conducted extensive experiments to theoretical and experimentalevaluations demonstrate the effectiveness of our scheme in terms of securityand performance.

Keywords: k-Nearest Neighbours (k-NN), DistributedTwo Trapdoors Public-Key Cryptosystem (DT-PKC).

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Issue Publication   On 30 th October 2019