Nearest Neighbor Search on Large-scale Data Using Skip Graph

Nearest neighbor search is one of the most fundamental queries on massive datasets, and it has extensive applications such as pattern recognition, statistical classification, graph algorithms, Location-Based Services and online recommendations. In this report, we propose a new searchable scheme, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted networks. Specifically, we modify the search algorithm of nearest neighbors with hybrid structures i.e Skip Graph-KD Tree, where the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. We address all the limitations in the previous works while still maintaining correctness and security. Moreover, our design is general, which can be used for secure knearest neighbor search, and it is compatible with other similar tree