Privacy preserving search index for image databases based on SURF and order preserving encryption

Abstract

Managing large personal image databases requires efficient privacy preserving indexing methods to allow for their outsourcing to possibly curious cloud servers. To construct a secure inverted index in this paper, first, visual words are extracted from stored images based on the Speeded-Up and Robust Features (SURF). Next, Order Preserving Encryption (OPE) is used to encipher the frequencies of occurrence of the extracted visual words. Another scale and rotation invariant feature, which is the local HSV histogram, is included for comparison. From the obtained results, it is apparent that SURF achieves more precise results. Aggregation of both features is considered to further improve the accuracy. The effects of the weighting scheme of the visual words and their number on the performance are investigated. Weighted term frequency inverse document frequency (tf-idf) together with the Jaccard similarity measure yield the best performance. OPE encryption is shown to have minor impact on the retrieval accuracy. To reduce encryption time, a lookup table is constructed. The inverted index reduces the search time significantly compared to a sequential search scheme as apparent from the results. A comparative study with recent related schemes demonstrates the competitiveness of the implemented system in terms of computational efficiency and accuracy.