Abstract
Approximate nearest neighbor (ANN) search in high-dimensional space has been studied extensively in recent years. However, it supports only ANN search over plaintext in traditional locality-sensitive hashing (LSH). How to perform ANN search over encrypted data becomes a new challenging task. In this paper, we make an attempt to formally address the problem. We propose a new secure and efficient ANN search scheme over encrypted data based on SortingKeys-LSH (SK-LSH) and mutable order-preserving encryption (mOPE). In our construction, a secure index is generated by incorporating SK-LSH with mOPE, which can simultaneously achieve efficient ANN search and ensure data confidentiality. Furthermore, the proposed solution can achieve efficient range query on encrypted data. Security analysis demonstrates that our construction can achieve the desired security properties.
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Notes
When \({\mathcal {L}} ({\mathcal {L}}>1)\) compound hash functions are adopted. \({\mathcal {L}}\) binary search tree is generated in the same way.
To simplify the discussion, we consider only one compound hash function adopted in the construction.
Based on the idea of SK-LSH, It can easily be derived that the page with the smallest distance to its corresponding compound hash key of the query point must be in {\(P_{L}\), \(P_{R}\)}.
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Acknowledgments
We are grateful to the anonymous referees for their invaluable suggestions. This work is supported by the National Natural Science Foundation of China (No. 61272455), China 111 Project (No. B08038), Doctoral Fund of Ministry of Education of China (No. 20130203110004), Program for New Century Excellent Talents in University (No. NCET-13-0946), and the Fundamental Research Funds for the Central Universities (Nos. BDY151402 and JB142001-14).
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Communicated by V. Loia.
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Wang, J., Miao, M., Gao, Y. et al. Enabling efficient approximate nearest neighbor search for outsourced database in cloud computing. Soft Comput 20, 4487–4495 (2016). https://doi.org/10.1007/s00500-015-1758-6
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DOI: https://doi.org/10.1007/s00500-015-1758-6