PPIS-JOIN: A Novel Privacy-Preserving Image Similarity Join Method
Autor: | Lei Zhu, Yangding Li, Fangxin Xie, Hao Yu, Jianfeng Zhang, Chengyuan Zhang |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Computer science General Neuroscience Nearest neighbor search Hash function Data security 02 engineering and technology Inverted index computer.software_genre 020901 industrial engineering & automation Similarity (network science) Artificial Intelligence Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Join (sigma algebra) 020201 artificial intelligence & image processing Affine transformation Data mining computer Software |
Zdroj: | Neural Processing Letters. 54:2783-2801 |
ISSN: | 1573-773X 1370-4621 |
Popis: | 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. |
Databáze: | OpenAIRE |
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