Secure k-NN Query on Encrypted Cloud Data with Multiple Keys - 2017

PROJECT TITLE :

Secure k-NN Query on Encrypted Cloud Data with Multiple Keys - 2017

ABSTRACT:

The k-nearest neighbors (k-NN) question could be a fundamental primitive in spatial and multimedia databases. It has intensive applications in location-based services, classification & clustering and therefore on. With the promise of confidentiality and privacy, massive knowledge are increasingly outsourced to cloud within the encrypted form for enjoying the benefits of Cloud Computing (e.g., cut back storage and question processing costs). Recently, several schemes have been proposed to support k-NN question on encrypted cloud knowledge. But, previous works have all assumed that the query users (QUs) are fully-trusted and apprehend the key of the info owner (DO), which is used to encrypt and decrypt outsourced data. The assumptions are unrealistic in several situations, since several users are neither trusted nor knowing the key. In this paper, we tend to propose a novel scheme for secure k-NN query on encrypted cloud knowledge with multiple keys, in that the DO and each QU all hold their own different keys, and don't share them with every different; meanwhile, the DO encrypts and decrypts outsourced information using the key of his own. Our scheme is constructed by a distributed 2 trapdoors public-key cryptosystem (DT-PKC) and a collection of protocols of secure 2-party computation, that not only preserves the information confidentiality and question privacy but conjointly supports the offline information owner. Our in depth theoretical and experimental evaluations demonstrate the effectiveness of our scheme in terms of security and performance.

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