Name
Abstract | ||
|---|---|---|
Recently, massive multimedia data (especially images) is moved to the cloud environment for analysis and retrieval, which makes data security issue become particularly significant. Image similarity join has attracted more and more attention in the community of multimedia retrieval. However, few researches have investigated the privacy-preserving problem of image similarity join. To tackle this challenge, this paper proposes a novel privacy-preserving image similarity join method, called PPIS-JOIN. Different from the existing schemes, this approach aims to combine deep image hashing method and a novel affine transformation method to conceal sensitive information at feature level and generate high quality hash codes. Meanwhile, based on secure hash codes, a privacy-preserving similarity query model is proposed, which includes a secure image hash codes based inverted index, called ISH-Index, to support efficient and accuracy similarity search. We conduct comprehensive experiments on three common used benchmarks, and the results demonstrate the performance of the proposed PPIS-JOIN outperforms baselines. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11063-021-10537-3 | Neural Processing Letters |
Keywords | DocType | Volume |
Privacy-preserving, Similarity join, Deep hashing, Image representation | Journal | 54 |
Issue | ISSN | Citations |
4 | 1370-4621 | 0 |
PageRank | References | Authors |
0.34 | 61 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengyuan Zhang | 1 | 0 | 0.68 |
| Fangxin Xie | 2 | 0 | 0.34 |
| Yu Yu | 3 | 69 | 19.95 |
| Jianfeng Zhang | 4 | 0 | 0.34 |
| Lei Zhu | 5 | 854 | 51.69 |
| Yangding Li | 6 | 0 | 1.01 |