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Adaptive Secure Nearest Neighbor Query Processing Over Encrypted Data
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DOI：10.1109/TDSC.2020.2998039.png)
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Abstract
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Nearest neighbor query processing is a fundamental problem that arises in many fields such as spatial databases and machine learning. This article aims to address the Secure Nearest Neighbor (SNN) problem in cloud computing. Prior SNN schemes are both insecure and inefficient. In this article, we formally prove and experimentally demonstrate that the SNN scheme ASPE is actually insecure against even ciphertext only attacks. Although prior work proved that it is impossible to construct an SNN scheme even in much relaxed standard security models, we point out the flaws of the hardness proof. We propose an SNN scheme and prove that it is secure against adaptive chosen keyword attacks. Our scheme is efficient as its query processing complexity is logarithmic. To evaluate the efficiency of our SNN scheme, we implemented our scheme in C++ and compared its performance with a plain text scheme, binary scheme, and a PIR scheme on a large set of over 10 million real-world data points. Experimental results show that our scheme is fast (0.124 millisecond per query when data set size is 10 million) and scalable in terms of the number of data points.
Keywords:
Cloud computing
Indexes
Adaptation models
Encryption
Data models
Cloud computing
secure nearest neighbor queries
adaptive IND-CKA security
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