EQFF: An Efficient Query Method Using Feature Fingerprints

The amount of data features is growing rapidly in the era of big data, posing challenges to both the security and efficiency of feature query. Most existing encryption-based retrieval approaches are limited by the significant computational overhead and merely support precise query, which might fail to handle the incomplete keywords and misspellings in the query. To achieve both query efficiency and privacy-preserving for large-scale data, this paper presents EQFF, an Efficient Query Method Using Feature Fingerprints. It converts varying-length features into fingerprints in the form of fixed-length vectors, and hence turns semantic information invisible to ensure query security. Based on the feature fingerprints, we further present the corresponding precise and fuzzy query approaches, design the inverted index library and propose a compression storage mechanism to improve query efficiency. Extensive experiments are conducted based on real-world datasets. Experimental results show that our EQFF takes only 6.4% memory compared with raw data, reduces the time cost from minutes to tens of milliseconds, and achieves an accuracy of 98% above.