Secure k Nearest Neighbors Query for High-Dimensional Vectors in Outsourced Environments

Due to the explosive increase of data in both the aspects of dimensionality and volume, performing $k$ nearest neighbors search over cloud environments has been progressively receiving more attention among researchers in the field of database cloud computing. However, the key challenge for switching $k$ nearest neighbors search from the local server (i.e., traditional way) to the third-party cloud is, that the database which always contains series of sensitive information has to be kept secret against the cloud. In this work, we present a pair of solutions towards Secure $k$ Nearest Neighbors (S $k$ NN) query in outsourced environments. By skillfully utilizing coarse quantization and the cryptography techniques Advanced Encryption Standard (AES) and Paillier homomorphic encryption, we construct a secure Inverted File (IVF) and compute encrypted approximate distances directly to search for high-dimensional data in the third-party cloud provider, and finally find the better tradeoff between the search quality and security. Empirical study over real datasets and practical environments validate our solutions’ feasibility, completeness, and practicality. Compared to the state-of-the-art, the proposed solutions resolve the S $k$ NN of high-dimensional data novelly, have very limited response time and provide high privacy protection on the side of both the User and the cloud provider.

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